#!/usr/bin/env python3 # -*- coding: utf-8 -*- # Copyright 2017--2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You may not # use this file except in compliance with the License. A copy of the License # is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file is distributed on # an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either # express or implied. See the License for the specific language governing # permissions and limitations under the License. """ SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores. Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text. It also knows all the standard test sets and handles downloading, processing, and tokenization for you. See the [README.md] file for more information. """ import argparse import functools import gzip import hashlib import io import logging import math import os import portalocker import re import sys import unicodedata import urllib.request from collections import Counter, namedtuple from itertools import zip_longest from typing import List, Iterable, Tuple, Union VERSION = "1.4.2" try: # SIGPIPE is not available on Windows machines, throwing an exception. from signal import SIGPIPE # If SIGPIPE is available, change behaviour to default instead of ignore. from signal import signal, SIG_DFL signal(SIGPIPE, SIG_DFL) except ImportError: logging.warning( "Could not import signal.SIGPIPE (this is expected on Windows machines)" ) # Where to store downloaded test sets. # Define the environment variable $SACREBLEU, or use the default of ~/.sacrebleu. # # Querying for a HOME environment variable can result in None (e.g., on Windows) # in which case the os.path.join() throws a TypeError. Using expanduser() is # a safe way to get the user's home folder. USERHOME = os.path.expanduser("~") SACREBLEU_DIR = os.environ.get("SACREBLEU", os.path.join(USERHOME, ".sacrebleu")) # n-gram order. Don't change this. NGRAM_ORDER = 4 # Default values for CHRF CHRF_ORDER = 6 # default to 2 (per http://www.aclweb.org/anthology/W16-2341) CHRF_BETA = 2 # The default floor value to use with `--smooth floor` SMOOTH_VALUE_DEFAULT = 0.0 # This defines data locations. # At the top level are test sets. # Beneath each test set, we define the location to download the test data. # The other keys are each language pair contained in the tarball, and the respective locations of the source and reference data within each. # Many of these are *.sgm files, which are processed to produced plain text that can be used by this script. # The canonical location of unpacked, processed data is $SACREBLEU_DIR/$TEST/$SOURCE-$TARGET.{$SOURCE,$TARGET} DATASETS = { "mtnt2019": { "data": ["http://www.cs.cmu.edu/~pmichel1/hosting/MTNT2019.tar.gz"], "description": "Test set for the WMT 19 robustness shared task", "md5": ["78a672e1931f106a8549023c0e8af8f6"], "en-fr": ["2:MTNT2019/en-fr.final.tsv", "3:MTNT2019/en-fr.final.tsv"], "fr-en": ["2:MTNT2019/fr-en.final.tsv", "3:MTNT2019/fr-en.final.tsv"], "en-ja": ["2:MTNT2019/en-ja.final.tsv", "3:MTNT2019/en-ja.final.tsv"], "ja-en": ["2:MTNT2019/ja-en.final.tsv", "3:MTNT2019/ja-en.final.tsv"], }, "mtnt1.1/test": { "data": [ "https://github.com/pmichel31415/mtnt/releases/download/v1.1/MTNT.1.1.tar.gz" ], "description": "Test data for the Machine Translation of Noisy Text task: http://www.cs.cmu.edu/~pmichel1/mtnt/", "citation": '@InProceedings{michel2018a:mtnt,\n author = "Michel, Paul and Neubig, Graham",\n title = "MTNT: A Testbed for Machine Translation of Noisy Text",\n booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",\n year = "2018",\n publisher = "Association for Computational Linguistics",\n pages = "543--553",\n location = "Brussels, Belgium",\n url = "http://aclweb.org/anthology/D18-1050"\n}', "md5": ["8ce1831ac584979ba8cdcd9d4be43e1d"], "en-fr": ["1:MTNT/test/test.en-fr.tsv", "2:MTNT/test/test.en-fr.tsv"], "fr-en": ["1:MTNT/test/test.fr-en.tsv", "2:MTNT/test/test.fr-en.tsv"], "en-ja": ["1:MTNT/test/test.en-ja.tsv", "2:MTNT/test/test.en-ja.tsv"], "ja-en": ["1:MTNT/test/test.ja-en.tsv", "2:MTNT/test/test.ja-en.tsv"], }, "mtnt1.1/valid": { "data": [ "https://github.com/pmichel31415/mtnt/releases/download/v1.1/MTNT.1.1.tar.gz" ], "description": "Validation data for the Machine Translation of Noisy Text task: http://www.cs.cmu.edu/~pmichel1/mtnt/", "citation": '@InProceedings{michel2018a:mtnt,\n author = "Michel, Paul and Neubig, Graham",\n title = "MTNT: A Testbed for Machine Translation of Noisy Text",\n booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",\n year = "2018",\n publisher = "Association for Computational Linguistics",\n pages = "543--553",\n location = "Brussels, Belgium",\n url = "http://aclweb.org/anthology/D18-1050"\n}', "md5": ["8ce1831ac584979ba8cdcd9d4be43e1d"], "en-fr": ["1:MTNT/valid/valid.en-fr.tsv", "2:MTNT/valid/valid.en-fr.tsv"], "fr-en": ["1:MTNT/valid/valid.fr-en.tsv", "2:MTNT/valid/valid.fr-en.tsv"], "en-ja": ["1:MTNT/valid/valid.en-ja.tsv", "2:MTNT/valid/valid.en-ja.tsv"], "ja-en": ["1:MTNT/valid/valid.ja-en.tsv", "2:MTNT/valid/valid.ja-en.tsv"], }, "mtnt1.1/train": { "data": [ "https://github.com/pmichel31415/mtnt/releases/download/v1.1/MTNT.1.1.tar.gz" ], "description": "Training data for the Machine Translation of Noisy Text task: http://www.cs.cmu.edu/~pmichel1/mtnt/", "citation": '@InProceedings{michel2018a:mtnt,\n author = "Michel, Paul and Neubig, Graham",\n title = "MTNT: A Testbed for Machine Translation of Noisy Text",\n booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",\n year = "2018",\n publisher = "Association for Computational Linguistics",\n pages = "543--553",\n location = "Brussels, Belgium",\n url = "http://aclweb.org/anthology/D18-1050"\n}', "md5": ["8ce1831ac584979ba8cdcd9d4be43e1d"], "en-fr": ["1:MTNT/train/train.en-fr.tsv", "2:MTNT/train/train.en-fr.tsv"], "fr-en": ["1:MTNT/train/train.fr-en.tsv", "2:MTNT/train/train.fr-en.tsv"], "en-ja": ["1:MTNT/train/train.en-ja.tsv", "2:MTNT/train/train.en-ja.tsv"], "ja-en": ["1:MTNT/train/train.ja-en.tsv", "2:MTNT/train/train.ja-en.tsv"], }, "wmt19": { "data": ["http://data.statmt.org/wmt19/translation-task/test.tgz"], "description": "Official evaluation data.", "md5": ["84de7162d158e28403103b01aeefc39a"], "cs-de": [ "sgm/newstest2019-csde-src.cs.sgm", "sgm/newstest2019-csde-ref.de.sgm", ], "de-cs": [ "sgm/newstest2019-decs-src.de.sgm", "sgm/newstest2019-decs-ref.cs.sgm", ], "de-en": [ "sgm/newstest2019-deen-src.de.sgm", "sgm/newstest2019-deen-ref.en.sgm", ], "de-fr": [ "sgm/newstest2019-defr-src.de.sgm", "sgm/newstest2019-defr-ref.fr.sgm", ], "en-cs": [ "sgm/newstest2019-encs-src.en.sgm", "sgm/newstest2019-encs-ref.cs.sgm", ], "en-de": [ "sgm/newstest2019-ende-src.en.sgm", "sgm/newstest2019-ende-ref.de.sgm", ], "en-fi": [ "sgm/newstest2019-enfi-src.en.sgm", "sgm/newstest2019-enfi-ref.fi.sgm", ], "en-gu": [ "sgm/newstest2019-engu-src.en.sgm", "sgm/newstest2019-engu-ref.gu.sgm", ], "en-kk": [ "sgm/newstest2019-enkk-src.en.sgm", "sgm/newstest2019-enkk-ref.kk.sgm", ], "en-lt": [ "sgm/newstest2019-enlt-src.en.sgm", "sgm/newstest2019-enlt-ref.lt.sgm", ], "en-ru": [ "sgm/newstest2019-enru-src.en.sgm", "sgm/newstest2019-enru-ref.ru.sgm", ], "en-zh": [ "sgm/newstest2019-enzh-src.en.sgm", "sgm/newstest2019-enzh-ref.zh.sgm", ], "fi-en": [ "sgm/newstest2019-fien-src.fi.sgm", "sgm/newstest2019-fien-ref.en.sgm", ], "fr-de": [ "sgm/newstest2019-frde-src.fr.sgm", "sgm/newstest2019-frde-ref.de.sgm", ], "gu-en": [ "sgm/newstest2019-guen-src.gu.sgm", "sgm/newstest2019-guen-ref.en.sgm", ], "kk-en": [ "sgm/newstest2019-kken-src.kk.sgm", "sgm/newstest2019-kken-ref.en.sgm", ], "lt-en": [ "sgm/newstest2019-lten-src.lt.sgm", "sgm/newstest2019-lten-ref.en.sgm", ], "ru-en": [ "sgm/newstest2019-ruen-src.ru.sgm", "sgm/newstest2019-ruen-ref.en.sgm", ], "zh-en": [ "sgm/newstest2019-zhen-src.zh.sgm", "sgm/newstest2019-zhen-ref.en.sgm", ], }, "wmt19/dev": { "data": ["http://data.statmt.org/wmt19/translation-task/dev.tgz"], "description": "Development data for tasks new to 2019.", "md5": ["f2ec7af5947c19e0cacb3882eb208002"], "lt-en": ["dev/newsdev2019-lten-src.lt.sgm", "dev/newsdev2019-lten-ref.en.sgm"], "en-lt": ["dev/newsdev2019-enlt-src.en.sgm", "dev/newsdev2019-enlt-ref.lt.sgm"], "gu-en": ["dev/newsdev2019-guen-src.gu.sgm", "dev/newsdev2019-guen-ref.en.sgm"], "en-gu": ["dev/newsdev2019-engu-src.en.sgm", "dev/newsdev2019-engu-ref.gu.sgm"], "kk-en": ["dev/newsdev2019-kken-src.kk.sgm", "dev/newsdev2019-kken-ref.en.sgm"], "en-kk": ["dev/newsdev2019-enkk-src.en.sgm", "dev/newsdev2019-enkk-ref.kk.sgm"], }, "wmt18": { "data": ["http://data.statmt.org/wmt18/translation-task/test.tgz"], "md5": ["f996c245ecffea23d0006fa4c34e9064"], "description": "Official evaluation data.", "citation": '@inproceedings{bojar-etal-2018-findings,\n title = "Findings of the 2018 Conference on Machine Translation ({WMT}18)",\n author = "Bojar, Ond{\v{r}}ej and\n Federmann, Christian and\n Fishel, Mark and\n Graham, Yvette and\n Haddow, Barry and\n Koehn, Philipp and\n Monz, Christof",\n booktitle = "Proceedings of the Third Conference on Machine Translation: Shared Task Papers",\n month = oct,\n year = "2018",\n address = "Belgium, Brussels",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/W18-6401",\n pages = "272--303",\n}', "cs-en": [ "test/newstest2018-csen-src.cs.sgm", "test/newstest2018-csen-ref.en.sgm", ], "de-en": [ "test/newstest2018-deen-src.de.sgm", "test/newstest2018-deen-ref.en.sgm", ], "en-cs": [ "test/newstest2018-encs-src.en.sgm", "test/newstest2018-encs-ref.cs.sgm", ], "en-de": [ "test/newstest2018-ende-src.en.sgm", "test/newstest2018-ende-ref.de.sgm", ], "en-et": [ "test/newstest2018-enet-src.en.sgm", "test/newstest2018-enet-ref.et.sgm", ], "en-fi": [ "test/newstest2018-enfi-src.en.sgm", "test/newstest2018-enfi-ref.fi.sgm", ], "en-ru": [ "test/newstest2018-enru-src.en.sgm", "test/newstest2018-enru-ref.ru.sgm", ], "et-en": [ "test/newstest2018-eten-src.et.sgm", "test/newstest2018-eten-ref.en.sgm", ], "fi-en": [ "test/newstest2018-fien-src.fi.sgm", "test/newstest2018-fien-ref.en.sgm", ], "ru-en": [ "test/newstest2018-ruen-src.ru.sgm", "test/newstest2018-ruen-ref.en.sgm", ], "en-tr": [ "test/newstest2018-entr-src.en.sgm", "test/newstest2018-entr-ref.tr.sgm", ], "tr-en": [ "test/newstest2018-tren-src.tr.sgm", "test/newstest2018-tren-ref.en.sgm", ], "en-zh": [ "test/newstest2018-enzh-src.en.sgm", "test/newstest2018-enzh-ref.zh.sgm", ], "zh-en": [ "test/newstest2018-zhen-src.zh.sgm", "test/newstest2018-zhen-ref.en.sgm", ], }, "wmt18/test-ts": { "data": ["http://data.statmt.org/wmt18/translation-task/test-ts.tgz"], "md5": ["5c621a34d512cc2dd74162ae7d00b320"], "description": "Official evaluation sources with extra test sets interleaved.", "cs-en": ["test/newstest2018-csen-src-ts.cs.sgm"], "de-en": ["test/newstest2018-deen-src-ts.de.sgm"], "en-cs": ["test/newstest2018-encs-src-ts.en.sgm"], "en-de": ["test/newstest2018-ende-src-ts.en.sgm"], "en-et": ["test/newstest2018-enet-src-ts.en.sgm"], "en-fi": ["test/newstest2018-enfi-src-ts.en.sgm"], "en-ru": ["test/newstest2018-enru-src-ts.en.sgm"], "et-en": ["test/newstest2018-eten-src-ts.et.sgm"], "fi-en": ["test/newstest2018-fien-src-ts.fi.sgm"], "ru-en": ["test/newstest2018-ruen-src-ts.ru.sgm"], "en-tr": ["test/newstest2018-entr-src-ts.en.sgm"], "tr-en": ["test/newstest2018-tren-src-ts.tr.sgm"], "en-zh": ["test/newstest2018-enzh-src-ts.en.sgm"], "zh-en": ["test/newstest2018-zhen-src-ts.zh.sgm"], }, "wmt18/dev": { "data": ["http://data.statmt.org/wmt18/translation-task/dev.tgz"], "md5": ["486f391da54a7a3247f02ebd25996f24"], "description": "Development data (Estonian<>English).", "et-en": ["dev/newsdev2018-eten-src.et.sgm", "dev/newsdev2018-eten-ref.en.sgm"], "en-et": ["dev/newsdev2018-enet-src.en.sgm", "dev/newsdev2018-enet-ref.et.sgm"], }, "wmt17": { "data": ["http://data.statmt.org/wmt17/translation-task/test.tgz"], "md5": ["86a1724c276004aa25455ae2a04cef26"], "description": "Official evaluation data.", "citation": "@InProceedings{bojar-EtAl:2017:WMT1,\n author = {Bojar, Ond\\v{r}ej and Chatterjee, Rajen and Federmann, Christian and Graham, Yvette and Haddow, Barry and Huang, Shujian and Huck, Matthias and Koehn, Philipp and Liu, Qun and Logacheva, Varvara and Monz, Christof and Negri, Matteo and Post, Matt and Rubino, Raphael and Specia, Lucia and Turchi, Marco},\n title = {Findings of the 2017 Conference on Machine Translation (WMT17)},\n booktitle = {Proceedings of the Second Conference on Machine Translation, Volume 2: Shared Task Papers},\n month = {September},\n year = {2017},\n address = {Copenhagen, Denmark},\n publisher = {Association for Computational Linguistics},\n pages = {169--214},\n url = {http://www.aclweb.org/anthology/W17-4717}\n}", "cs-en": [ "test/newstest2017-csen-src.cs.sgm", "test/newstest2017-csen-ref.en.sgm", ], "de-en": [ "test/newstest2017-deen-src.de.sgm", "test/newstest2017-deen-ref.en.sgm", ], "en-cs": [ "test/newstest2017-encs-src.en.sgm", "test/newstest2017-encs-ref.cs.sgm", ], "en-de": [ "test/newstest2017-ende-src.en.sgm", "test/newstest2017-ende-ref.de.sgm", ], "en-fi": [ "test/newstest2017-enfi-src.en.sgm", "test/newstest2017-enfi-ref.fi.sgm", ], "en-lv": [ "test/newstest2017-enlv-src.en.sgm", "test/newstest2017-enlv-ref.lv.sgm", ], "en-ru": [ "test/newstest2017-enru-src.en.sgm", "test/newstest2017-enru-ref.ru.sgm", ], "en-tr": [ "test/newstest2017-entr-src.en.sgm", "test/newstest2017-entr-ref.tr.sgm", ], "en-zh": [ "test/newstest2017-enzh-src.en.sgm", "test/newstest2017-enzh-ref.zh.sgm", ], "fi-en": [ "test/newstest2017-fien-src.fi.sgm", "test/newstest2017-fien-ref.en.sgm", ], "lv-en": [ "test/newstest2017-lven-src.lv.sgm", "test/newstest2017-lven-ref.en.sgm", ], "ru-en": [ "test/newstest2017-ruen-src.ru.sgm", "test/newstest2017-ruen-ref.en.sgm", ], "tr-en": [ "test/newstest2017-tren-src.tr.sgm", "test/newstest2017-tren-ref.en.sgm", ], "zh-en": [ "test/newstest2017-zhen-src.zh.sgm", "test/newstest2017-zhen-ref.en.sgm", ], }, "wmt17/B": { "data": ["http://data.statmt.org/wmt17/translation-task/test.tgz"], "md5": ["86a1724c276004aa25455ae2a04cef26"], "description": "Additional reference for EN-FI and FI-EN.", "en-fi": [ "test/newstestB2017-enfi-src.en.sgm", "test/newstestB2017-enfi-ref.fi.sgm", ], }, "wmt17/tworefs": { "data": ["http://data.statmt.org/wmt17/translation-task/test.tgz"], "md5": ["86a1724c276004aa25455ae2a04cef26"], "description": "Systems with two references.", "en-fi": [ "test/newstest2017-enfi-src.en.sgm", "test/newstest2017-enfi-ref.fi.sgm", "test/newstestB2017-enfi-ref.fi.sgm", ], }, "wmt17/improved": { "data": ["http://data.statmt.org/wmt17/translation-task/test-update-1.tgz"], "md5": ["91dbfd5af99bc6891a637a68e04dfd41"], "description": "Improved zh-en and en-zh translations.", "en-zh": ["newstest2017-enzh-src.en.sgm", "newstest2017-enzh-ref.zh.sgm"], "zh-en": ["newstest2017-zhen-src.zh.sgm", "newstest2017-zhen-ref.en.sgm"], }, "wmt17/dev": { "data": ["http://data.statmt.org/wmt17/translation-task/dev.tgz"], "md5": ["9b1aa63c1cf49dccdd20b962fe313989"], "description": "Development sets released for new languages in 2017.", "en-lv": ["dev/newsdev2017-enlv-src.en.sgm", "dev/newsdev2017-enlv-ref.lv.sgm"], "en-zh": ["dev/newsdev2017-enzh-src.en.sgm", "dev/newsdev2017-enzh-ref.zh.sgm"], "lv-en": ["dev/newsdev2017-lven-src.lv.sgm", "dev/newsdev2017-lven-ref.en.sgm"], "zh-en": ["dev/newsdev2017-zhen-src.zh.sgm", "dev/newsdev2017-zhen-ref.en.sgm"], }, "wmt17/ms": { "data": [ "https://github.com/MicrosoftTranslator/Translator-HumanParityData/archive/master.zip", "http://data.statmt.org/wmt17/translation-task/test-update-1.tgz", ], "md5": ["18fdaa7a3c84cf6ef688da1f6a5fa96f", "91dbfd5af99bc6891a637a68e04dfd41"], "description": "Additional Chinese-English references from Microsoft Research.", "citation": "@inproceedings{achieving-human-parity-on-automatic-chinese-to-english-news-translation,\n author = {Hassan Awadalla, Hany and Aue, Anthony and Chen, Chang and Chowdhary, Vishal and Clark, Jonathan and Federmann, Christian and Huang, Xuedong and Junczys-Dowmunt, Marcin and Lewis, Will and Li, Mu and Liu, Shujie and Liu, Tie-Yan and Luo, Renqian and Menezes, Arul and Qin, Tao and Seide, Frank and Tan, Xu and Tian, Fei and Wu, Lijun and Wu, Shuangzhi and Xia, Yingce and Zhang, Dongdong and Zhang, Zhirui and Zhou, Ming},\n title = {Achieving Human Parity on Automatic Chinese to English News Translation},\n booktitle = {},\n year = {2018},\n month = {March},\n abstract = {Machine translation has made rapid advances in recent years. Millions of people are using it today in online translation systems and mobile applications in order to communicate across language barriers. The question naturally arises whether such systems can approach or achieve parity with human translations. In this paper, we first address the problem of how to define and accurately measure human parity in translation. We then describe Microsoft’s machine translation system and measure the quality of its translations on the widely used WMT 2017 news translation task from Chinese to English. We find that our latest neural machine translation system has reached a new state-of-the-art, and that the translation quality is at human parity when compared to professional human translations. We also find that it significantly exceeds the quality of crowd-sourced non-professional translations.},\n publisher = {},\n url = {https://www.microsoft.com/en-us/research/publication/achieving-human-parity-on-automatic-chinese-to-english-news-translation/},\n address = {},\n pages = {},\n journal = {},\n volume = {},\n chapter = {},\n isbn = {},\n}", "zh-en": [ "newstest2017-zhen-src.zh.sgm", "newstest2017-zhen-ref.en.sgm", "Translator-HumanParityData-master/Translator-HumanParityData/References/Translator-HumanParityData-Reference-HT.txt", "Translator-HumanParityData-master/Translator-HumanParityData/References/Translator-HumanParityData-Reference-PE.txt", ], }, "wmt16": { "data": ["http://data.statmt.org/wmt16/translation-task/test.tgz"], "md5": ["3d809cd0c2c86adb2c67034d15c4e446"], "description": "Official evaluation data.", "citation": "@InProceedings{bojar-EtAl:2016:WMT1,\n author = {Bojar, Ond\\v{r}ej and Chatterjee, Rajen and Federmann, Christian and Graham, Yvette and Haddow, 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SUBSETS = { "wmt18": "rt.com.68098=US-crime guardian.181611=US-politics bbc.310963=GB-sport washpost.116881=US-politics scotsman.104228=GB-sport timemagazine.75207=OTHER-world-ID " "euronews-en.117981=OTHER-crime-AE smh.com.au.242810=US-crime msnbc.53726=US-politics euronews-en.117983=US-politics msnbc.53894=US-crime theglobeandmail.com.62700=US-business " "bbc.310870=OTHER-world-AF reuters.196698=US-politics latimes.231739=US-sport thelocal.51929=OTHER-world-SE cbsnews.198694=US-politics reuters.196718=OTHER-sport-RU " "abcnews.255599=EU-sport nytimes.127256=US-entertainment scotsman.104225=GB-politics dailymail.co.uk.233026=GB-scitech independent.181088=GB-entertainment " "brisbanetimes.com.au.181614=OTHER-business-AU washpost.116837=US-politics dailymail.co.uk.232928=GB-world thelocal.51916=OTHER-politics-IT bbc.310871=US-crime " "nytimes.127392=EU-business-DE euronews-en.118001=EU-scitech-FR washpost.116866=OTHER-crime-MX dailymail.co.uk.233025=OTHER-scitech-CA latimes.231829=US-crime " "guardian.181662=US-entertainment msnbc.53731=US-crime rt.com.68127=OTHER-sport-RU latimes.231782=US-business latimes.231840=US-sport reuters.196711=OTHER-scitech " "guardian.181666=GB-entertainment novinite.com.24019=US-politics smh.com.au.242750=OTHER-scitech guardian.181610=US-politics telegraph.364393=OTHER-crime-ZA " "novinite.com.23995=EU-world dailymail.co.uk.233028=GB-scitech independent.181071=GB-sport telegraph.364538=GB-scitech timemagazine.75193=US-politics " "independent.181096=US-entertainment upi.140602=OTHER-world-AF bbc.310946=GB-business independent.181052=EU-sport ", "wmt19": "bbc.381790=GB-politics rt.com.91337=OTHER-politics-MK nytimes.184853=US-world upi.176266=US-crime guardian.221754=GB-business dailymail.co.uk.298595=GB-business " "cnbc.com.6790=US-politics nytimes.184837=OTHER-world-ID upi.176249=GB-sport euronews-en.153835=OTHER-world-ID dailymail.co.uk.298732=GB-crime telegraph.405401=GB-politics " "newsweek.51331=OTHER-crime-CN abcnews.306815=US-world cbsnews.248384=US-politics reuters.218882=GB-politics cbsnews.248387=US-crime abcnews.306764=OTHER-world-MX " "reuters.218888=EU-politics bbc.381780=GB-crime bbc.381746=GB-sport euronews-en.153800=EU-politics bbc.381679=GB-crime bbc.381735=GB-crime newsweek.51338=US-world " "bbc.381765=GB-crime cnn.304489=US-politics reuters.218863=OTHER-world-ID nytimes.184860=OTHER-world-ID cnn.304404=US-crime bbc.381647=US-entertainment " "abcnews.306758=OTHER-politics-MX cnbc.com.6772=US-business reuters.218932=OTHER-politics-MK upi.176251=GB-sport reuters.218921=US-sport cnn.304447=US-politics " "guardian.221679=GB-politics scotsman.133765=GB-sport scotsman.133804=GB-entertainment guardian.221762=OTHER-politics-BO cnbc.com.6769=US-politics " "dailymail.co.uk.298692=EU-entertainment scotsman.133744=GB-world reuters.218911=US-sport newsweek.51310=US-politics independent.226301=US-sport reuters.218923=EU-sport " "reuters.218861=US-politics dailymail.co.uk.298759=US-world scotsman.133791=GB-sport cbsnews.248484=EU-scitech dailymail.co.uk.298630=US-scitech " "newsweek.51329=US-entertainment bbc.381701=GB-crime dailymail.co.uk.298738=GB-entertainment bbc.381669=OTHER-world-CN foxnews.94512=US-politics " "guardian.221718=GB-entertainment dailymail.co.uk.298686=GB-politics cbsnews.248471=US-politics newsweek.51318=US-entertainment rt.com.91335=US-politics " "newsweek.51300=US-politics cnn.304478=US-politics upi.176275=US-politics telegraph.405422=OTHER-world-ID reuters.218933=US-politics newsweek.51328=US-politics " "newsweek.51307=US-business bbc.381692=GB-world independent.226346=GB-entertainment bbc.381646=GB-sport reuters.218914=US-sport scotsman.133758=EU-sport " "rt.com.91350=EU-world scotsman.133773=GB-scitech rt.com.91334=EU-crime bbc.381680=GB-politics guardian.221756=US-politics scotsman.133783=GB-politics cnn.304521=US-sport " "dailymail.co.uk.298622=GB-politics bbc.381789=GB-sport dailymail.co.uk.298644=GB-business dailymail.co.uk.298602=GB-world scotsman.133753=GB-sport " "independent.226317=GB-entertainment nytimes.184862=US-politics thelocal.65969=OTHER-world-SY nytimes.184825=US-politics cnbc.com.6784=US-politics nytimes.184804=US-politics " "nytimes.184830=US-politics scotsman.133801=GB-sport cnbc.com.6770=US-business bbc.381760=GB-crime reuters.218865=OTHER-world-ID newsweek.51339=US-crime " "euronews-en.153797=OTHER-world-ID abcnews.306774=US-crime dailymail.co.uk.298696=GB-politics abcnews.306755=US-politics reuters.218909=US-crime " "independent.226349=OTHER-sport-RU newsweek.51330=US-politics bbc.381705=GB-sport newsweek.51340=OTHER-world-ID cbsnews.248411=OTHER-world-FM abcnews.306776=US-crime " "bbc.381694=GB-entertainment rt.com.91356=US-world telegraph.405430=GB-entertainment telegraph.405404=EU-world bbc.381749=GB-world telegraph.405413=US-politics " "bbc.381736=OTHER-politics-KP cbsnews.248394=US-politics nytimes.184822=US-world telegraph.405408=US-politics euronews-en.153799=OTHER-politics-SY " "euronews-en.153826=EU-sport cnn.304400=US-world", } SUBSETS = { k: {d.split("=")[0]: d.split("=")[1] for d in v.split()} for (k, v) in SUBSETS.items() } COUNTRIES = sorted(list({v.split("-")[0] for v in SUBSETS["wmt19"].values()})) DOMAINS = sorted(list({v.split("-")[1] for v in SUBSETS["wmt19"].values()})) def tokenize_13a(line): """ Tokenizes an input line using a relatively minimal tokenization that is however equivalent to mteval-v13a, used by WMT. :param line: a segment to tokenize :return: the tokenized line """ norm = line # language-independent part: norm = norm.replace("", "") norm = norm.replace("-\n", "") norm = norm.replace("\n", " ") norm = norm.replace(""", '"') norm = norm.replace("&", "&") norm = norm.replace("<", "<") norm = norm.replace(">", ">") # language-dependent part (assuming Western languages): norm = " {} ".format(norm) norm = re.sub(r"([\{-\~\[-\` -\&\(-\+\:-\@\/])", " \\1 ", norm) norm = re.sub( r"([^0-9])([\.,])", "\\1 \\2 ", norm ) # tokenize period and comma unless preceded by a digit norm = re.sub( r"([\.,])([^0-9])", " \\1 \\2", norm ) # tokenize period and comma unless followed by a digit norm = re.sub( r"([0-9])(-)", "\\1 \\2 ", norm ) # tokenize dash when preceded by a digit norm = re.sub(r"\s+", " ", norm) # one space only between words norm = re.sub(r"^\s+", "", norm) # no leading space norm = re.sub(r"\s+$", "", norm) # no trailing space return norm class UnicodeRegex: """Ad-hoc hack to recognize all punctuation and symbols. without depending on https://pypi.python.org/pypi/regex/.""" @staticmethod def _property_chars(prefix): return "".join( chr(x) for x in range(sys.maxunicode) if unicodedata.category(chr(x)).startswith(prefix) ) @staticmethod @functools.lru_cache(maxsize=1) def punctuation(): return UnicodeRegex._property_chars("P") @staticmethod @functools.lru_cache(maxsize=1) def nondigit_punct_re(): return re.compile(r"([^\d])([" + UnicodeRegex.punctuation() + r"])") @staticmethod @functools.lru_cache(maxsize=1) def punct_nondigit_re(): return re.compile(r"([" + UnicodeRegex.punctuation() + r"])([^\d])") @staticmethod @functools.lru_cache(maxsize=1) def symbol_re(): return re.compile("([" + UnicodeRegex._property_chars("S") + "])") def tokenize_v14_international(string): r"""Tokenize a string following the official BLEU implementation. See https://github.com/moses-smt/mosesdecoder/blob/master/scripts/generic/mteval-v14.pl#L954-L983 In our case, the input string is expected to be just one line and no HTML entities de-escaping is needed. So we just tokenize on punctuation and symbols, except when a punctuation is preceded and followed by a digit (e.g. a comma/dot as a thousand/decimal separator). Note that a number (e.g., a year) followed by a dot at the end of sentence is NOT tokenized, i.e. the dot stays with the number because `s/(\p{P})(\P{N})/ $1 $2/g` does not match this case (unless we add a space after each sentence). However, this error is already in the original mteval-v14.pl and we want to be consistent with it. The error is not present in the non-international version, which uses `$norm_text = " $norm_text "` (or `norm = " {} ".format(norm)` in Python). :param string: the input string :return: a list of tokens """ string = UnicodeRegex.nondigit_punct_re().sub(r"\1 \2 ", string) string = UnicodeRegex.punct_nondigit_re().sub(r" \1 \2", string) string = UnicodeRegex.symbol_re().sub(r" \1 ", string) return string.strip() def tokenize_zh(sentence): """MIT License Copyright (c) 2017 - Shujian Huang Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. The tokenization of Chinese text in this script contains two steps: separate each Chinese characters (by utf-8 encoding); tokenize the non Chinese part (following the mteval script). Author: Shujian Huang huangsj@nju.edu.cn :param sentence: input sentence :return: tokenized sentence """ def is_chinese_char(uchar): """ :param uchar: input char in unicode :return: whether the input char is a Chinese character. """ if ( uchar >= u"\u3400" and uchar <= u"\u4db5" ): # CJK Unified Ideographs Extension A, release 3.0 return True elif ( uchar >= u"\u4e00" and uchar <= u"\u9fa5" ): # CJK Unified Ideographs, release 1.1 return True elif ( uchar >= u"\u9fa6" and uchar <= u"\u9fbb" ): # CJK Unified Ideographs, release 4.1 return True elif ( uchar >= u"\uf900" and uchar <= u"\ufa2d" ): # CJK Compatibility Ideographs, release 1.1 return True elif ( uchar >= u"\ufa30" and uchar <= u"\ufa6a" ): # CJK Compatibility Ideographs, release 3.2 return True elif ( uchar >= u"\ufa70" and uchar <= u"\ufad9" ): # CJK Compatibility Ideographs, release 4.1 return True elif ( uchar >= u"\u20000" and uchar <= u"\u2a6d6" ): # CJK Unified Ideographs Extension B, release 3.1 return True elif ( uchar >= u"\u2f800" and uchar <= u"\u2fa1d" ): # CJK Compatibility Supplement, release 3.1 return True elif ( uchar >= u"\uff00" and uchar <= u"\uffef" ): # Full width ASCII, full width of English punctuation, half width Katakana, half wide half width kana, Korean alphabet return True elif uchar >= u"\u2e80" and uchar <= u"\u2eff": # CJK Radicals Supplement return True elif uchar >= u"\u3000" and uchar <= u"\u303f": # CJK punctuation mark return True elif uchar >= u"\u31c0" and uchar <= u"\u31ef": # CJK stroke return True elif uchar >= u"\u2f00" and uchar <= u"\u2fdf": # Kangxi Radicals return True elif uchar >= u"\u2ff0" and uchar <= u"\u2fff": # Chinese character structure return True elif uchar >= u"\u3100" and uchar <= u"\u312f": # Phonetic symbols return True elif ( uchar >= u"\u31a0" and uchar <= u"\u31bf" ): # Phonetic symbols (Taiwanese and Hakka expansion) return True elif uchar >= u"\ufe10" and uchar <= u"\ufe1f": return True elif uchar >= u"\ufe30" and uchar <= u"\ufe4f": return True elif uchar >= u"\u2600" and uchar <= u"\u26ff": return True elif uchar >= u"\u2700" and uchar <= u"\u27bf": return True elif uchar >= u"\u3200" and uchar <= u"\u32ff": return True elif uchar >= u"\u3300" and uchar <= u"\u33ff": return True return False sentence = sentence.strip() sentence_in_chars = "" for char in sentence: if is_chinese_char(char): sentence_in_chars += " " sentence_in_chars += char sentence_in_chars += " " else: sentence_in_chars += char sentence = sentence_in_chars # TODO: the code above could probably be replaced with the following line: # import regex # sentence = regex.sub(r'(\p{Han})', r' \1 ', sentence) # tokenize punctuation sentence = re.sub(r"([\{-\~\[-\` -\&\(-\+\:-\@\/])", r" \1 ", sentence) # tokenize period and comma unless preceded by a digit sentence = re.sub(r"([^0-9])([\.,])", r"\1 \2 ", sentence) # tokenize period and comma unless followed by a digit sentence = re.sub(r"([\.,])([^0-9])", r" \1 \2", sentence) # tokenize dash when preceded by a digit sentence = re.sub(r"([0-9])(-)", r"\1 \2 ", sentence) # one space only between words sentence = re.sub(r"\s+", r" ", sentence) # no leading or trailing spaces sentence = sentence.strip() return sentence TOKENIZERS = { "13a": tokenize_13a, "intl": tokenize_v14_international, "zh": tokenize_zh, "none": lambda x: x, } DEFAULT_TOKENIZER = "13a" def smart_open(file, mode="rt", encoding="utf-8"): """Convenience function for reading compressed or plain text files. :param file: The file to read. :param mode: The file mode (read, write). :param encoding: The file encoding. """ if file.endswith(".gz"): return gzip.open(file, mode=mode, encoding=encoding, newline="\n") return open(file, mode=mode, encoding=encoding, newline="\n") def my_log(num): """ Floors the log function :param num: the number :return: log(num) floored to a very low number """ if num == 0.0: return -9999999999 return math.log(num) def bleu_signature(args, numrefs): """ Builds a signature that uniquely identifies the scoring parameters used. :param args: the arguments passed into the script :return: the signature """ # Abbreviations for the signature abbr = { "test": "t", "lang": "l", "smooth": "s", "case": "c", "tok": "tok", "numrefs": "#", "version": "v", "origlang": "o", "subset": "S", } signature = { "tok": args.tokenize, "version": VERSION, "smooth": args.smooth, "numrefs": numrefs, "case": "lc" if args.lc else "mixed", } if args.test_set is not None: signature["test"] = args.test_set if args.langpair is not None: signature["lang"] = args.langpair if args.origlang is not None: signature["origlang"] = args.origlang if args.subset is not None: signature["subset"] = args.subset sigstr = "+".join( [ "{}.{}".format(abbr[x] if args.short else x, signature[x]) for x in sorted(signature.keys()) ] ) return sigstr def chrf_signature(args, numrefs): """ Builds a signature that uniquely identifies the scoring parameters used. :param args: the arguments passed into the script :return: the chrF signature """ # Abbreviations for the signature abbr = { "test": "t", "lang": "l", "numchars": "n", "space": "s", "case": "c", "numrefs": "#", "version": "v", "origlang": "o", "subset": "S", } signature = { "version": VERSION, "space": args.chrf_whitespace, "numchars": args.chrf_order, "numrefs": numrefs, "case": "lc" if args.lc else "mixed", } if args.test_set is not None: signature["test"] = args.test_set if args.langpair is not None: signature["lang"] = args.langpair if args.origlang is not None: signature["origlang"] = args.origlang if args.subset is not None: signature["subset"] = args.subset sigstr = "+".join( [ "{}.{}".format(abbr[x] if args.short else x, signature[x]) for x in sorted(signature.keys()) ] ) return sigstr def extract_ngrams(line, min_order=1, max_order=NGRAM_ORDER) -> Counter: """Extracts all the ngrams (min_order <= n <= max_order) from a sequence of tokens. :param line: A segment containing a sequence of words. :param min_order: Minimum n-gram length (default: 1). :param max_order: Maximum n-gram length (default: NGRAM_ORDER). :return: a dictionary containing ngrams and counts """ ngrams = Counter() tokens = line.split() for n in range(min_order, max_order + 1): for i in range(0, len(tokens) - n + 1): ngram = " ".join(tokens[i : i + n]) ngrams[ngram] += 1 return ngrams def extract_char_ngrams(s: str, n: int) -> Counter: """ Yields counts of character n-grams from string s of order n. """ return Counter([s[i : i + n] for i in range(len(s) - n + 1)]) def ref_stats(output, refs): ngrams = Counter() closest_diff = None closest_len = None for ref in refs: tokens = ref.split() reflen = len(tokens) diff = abs(len(output.split()) - reflen) if closest_diff is None or diff < closest_diff: closest_diff = diff closest_len = reflen elif diff == closest_diff: if reflen < closest_len: closest_len = reflen ngrams_ref = extract_ngrams(ref) for ngram in ngrams_ref.keys(): ngrams[ngram] = max(ngrams[ngram], ngrams_ref[ngram]) return ngrams, closest_diff, closest_len def _clean(s): """ Removes trailing and leading spaces and collapses multiple consecutive internal spaces to a single one. :param s: The string. :return: A cleaned-up string. """ return re.sub(r"\s+", " ", s.strip()) def process_to_text(rawfile, txtfile, field: int = None): """Processes raw files to plain text files. :param rawfile: the input file (possibly SGML) :param txtfile: the plaintext file :param field: For TSV files, which field to extract. """ if not os.path.exists(txtfile) or os.path.getsize(txtfile) == 0: logging.info("Processing %s to %s", rawfile, txtfile) if rawfile.endswith(".sgm") or rawfile.endswith(".sgml"): with smart_open(rawfile) as fin, smart_open(txtfile, "wt") as fout: for line in fin: if line.startswith("(.*).*?", "\\1", line)), file=fout, ) elif rawfile.endswith(".xml"): # IWSLT with smart_open(rawfile) as fin, smart_open(txtfile, "wt") as fout: for line in fin: if line.startswith("(.*).*?", "\\1", line)), file=fout, ) elif rawfile.endswith(".txt"): # wmt17/ms with smart_open(rawfile) as fin, smart_open(txtfile, "wt") as fout: for line in fin: print(line.rstrip(), file=fout) elif rawfile.endswith(".tsv"): # MTNT with smart_open(rawfile) as fin, smart_open(txtfile, "wt") as fout: for line in fin: print(line.rstrip().split("\t")[field], file=fout) def print_test_set(test_set, langpair, side, origlang=None, subset=None): """Prints to STDOUT the specified side of the specified test set :param test_set: the test set to print :param langpair: the language pair :param side: 'src' for source, 'ref' for reference :param origlang: print only sentences with a given original language (2-char ISO639-1 code), "non-" prefix means negation :param subset: print only sentences whose document annotation matches a given regex """ files = download_test_set(test_set, langpair) if side == "src": files = [files[0]] elif side == "ref": files.pop(0) streams = [smart_open(file) for file in files] streams = _filter_subset(streams, test_set, langpair, origlang, subset) for lines in zip(*streams): print("\t".join(map(lambda x: x.rstrip(), lines))) def download_test_set(test_set, langpair=None): """Downloads the specified test to the system location specified by the SACREBLEU environment variable. :param test_set: the test set to download :param langpair: the language pair (needed for some datasets) :return: the set of processed files """ outdir = os.path.join(SACREBLEU_DIR, test_set) os.makedirs(outdir, exist_ok=True) expected_checksums = DATASETS[test_set].get("md5", [None] * len(DATASETS[test_set])) for dataset, expected_md5 in zip(DATASETS[test_set]["data"], expected_checksums): tarball = os.path.join(outdir, os.path.basename(dataset)) rawdir = os.path.join(outdir, "raw") lockfile = "{}.lock".format(tarball) with portalocker.Lock(lockfile, "w", timeout=60): if not os.path.exists(tarball) or os.path.getsize(tarball) == 0: logging.info("Downloading %s to %s", dataset, tarball) try: with urllib.request.urlopen(dataset) as f, open( tarball, "wb" ) as out: out.write(f.read()) except ssl.SSLError: logging.warning( "An SSL error was encountered in downloading the files. If you're on a Mac, " 'you may need to run the "Install Certificates.command" file located in the ' '"Python 3" folder, often found under /Applications' ) sys.exit(1) # Check md5sum if expected_md5 is not None: md5 = hashlib.md5() with open(tarball, "rb") as infile: for line in infile: md5.update(line) if md5.hexdigest() != expected_md5: logging.error( "Fatal: MD5 sum of downloaded file was incorrect (got {}, expected {}).".format( md5.hexdigest(), expected_md5 ) ) logging.error( 'Please manually delete "{}" and rerun the command.'.format( tarball ) ) logging.error( "If the problem persists, the tarball may have changed, in which case, please contact the SacreBLEU maintainer." ) sys.exit(1) else: logging.info("Checksum passed: {}".format(md5.hexdigest())) # Extract the tarball logging.info("Extracting %s", tarball) if tarball.endswith(".tar.gz") or tarball.endswith(".tgz"): import tarfile tar = tarfile.open(tarball) tar.extractall(path=rawdir) elif tarball.endswith(".zip"): import zipfile zipfile = zipfile.ZipFile(tarball, "r") zipfile.extractall(path=rawdir) zipfile.close() found = [] # Process the files into plain text languages = DATASETS[test_set].keys() if langpair is None else [langpair] for pair in languages: if "-" not in pair: continue src, tgt = pair.split("-") rawfile = DATASETS[test_set][pair][0] field = None # used for TSV files if rawfile.endswith(".tsv"): field, rawfile = rawfile.split(":", maxsplit=1) field = int(field) rawpath = os.path.join(rawdir, rawfile) outpath = os.path.join(outdir, "{}.{}".format(pair, src)) process_to_text(rawpath, outpath, field=field) found.append(outpath) refs = DATASETS[test_set][pair][1:] for i, ref in enumerate(refs): field = None if ref.endswith(".tsv"): field, ref = ref.split(":", maxsplit=1) field = int(field) rawpath = os.path.join(rawdir, ref) if len(refs) >= 2: outpath = os.path.join(outdir, "{}.{}.{}".format(pair, tgt, i)) else: outpath = os.path.join(outdir, "{}.{}".format(pair, tgt)) process_to_text(rawpath, outpath, field=field) found.append(outpath) return found class Result: def __init__(self, score: float): self.score = score def __str__(self): return self.format() class BLEU: def __init__(self, scores, counts, totals, precisions, bp, sys_len, ref_len): self.scores = scores self.counts = counts self.totals = totals self.precisions = precisions self.bp = bp self.sys_len = sys_len self.ref_len = ref_len def format(self, width=2): precisions = "/".join(["{:.1f}".format(p) for p in self.precisions]) return "BLEU = {scores} {precisions} (BP = {bp:.3f} ratio = {ratio:.3f} hyp_len = {sys_len:d} ref_len = {ref_len:d})".format( scores=self.scores, width=width, precisions=precisions, bp=self.bp, ratio=self.sys_len / self.ref_len, sys_len=self.sys_len, ref_len=self.ref_len, ) class CHRF(Result): def __init__(self, score: float): super().__init__(score) def format(self, width=2): return "{score:.{width}f}".format(score=self.score, width=width) def compute_bleu( correct: List[int], total: List[int], sys_len: int, ref_len: int, smooth_method="none", smooth_value=SMOOTH_VALUE_DEFAULT, use_effective_order=False, ) -> BLEU: """Computes BLEU score from its sufficient statistics. Adds smoothing. Smoothing methods (citing "A Systematic Comparison of Smoothing Techniques for Sentence-Level BLEU", Boxing Chen and Colin Cherry, WMT 2014: http://aclweb.org/anthology/W14-3346) - exp: NIST smoothing method (Method 3) - floor: Method 1 - add-k: Method 2 (generalizing Lin and Och, 2004) - none: do nothing. :param correct: List of counts of correct ngrams, 1 <= n <= NGRAM_ORDER :param total: List of counts of total ngrams, 1 <= n <= NGRAM_ORDER :param sys_len: The cumulative system length :param ref_len: The cumulative reference length :param smooth: The smoothing method to use :param smooth_value: The smoothing value added, if smooth method 'floor' is used :param use_effective_order: If true, use the length of `correct` for the n-gram order instead of NGRAM_ORDER. :return: A BLEU object with the score (100-based) and other statistics. """ precisions = [0 for x in range(NGRAM_ORDER)] smooth_mteval = 1.0 effective_order = NGRAM_ORDER for n in range(NGRAM_ORDER): if smooth_method == "add-k" and n > 1: correct[n] += smooth_value total[n] += smooth_value if total[n] == 0: break if use_effective_order: effective_order = n + 1 if correct[n] == 0: if smooth_method == "exp": smooth_mteval *= 2 precisions[n] = 100.0 / (smooth_mteval * total[n]) elif smooth_method == "floor": precisions[n] = 100.0 * smooth_value / total[n] else: precisions[n] = 100.0 * correct[n] / total[n] # If the system guesses no i-grams, 1 <= i <= NGRAM_ORDER, the BLEU score is 0 (technically undefined). # This is a problem for sentence-level BLEU or a corpus of short sentences, where systems will get no credit # if sentence lengths fall under the NGRAM_ORDER threshold. This fix scales NGRAM_ORDER to the observed # maximum order. It is only available through the API and off by default brevity_penalty = 1.0 if sys_len < ref_len: brevity_penalty = math.exp(1 - ref_len / sys_len) if sys_len > 0 else 0.0 scores = [] for effective_order in range(1, NGRAM_ORDER + 1): scores.append( brevity_penalty * math.exp(sum(map(my_log, precisions[:effective_order])) / effective_order) ) return BLEU(scores, correct, total, precisions, brevity_penalty, sys_len, ref_len) def sentence_bleu( hypothesis: str, references: List[str], smooth_method: str = "floor", smooth_value: float = SMOOTH_VALUE_DEFAULT, use_effective_order: bool = True, ) -> BLEU: """ Computes BLEU on a single sentence pair. Disclaimer: computing BLEU on the sentence level is not its intended use, BLEU is a corpus-level metric. :param hypothesis: Hypothesis string. :param reference: Reference string. :param smooth_value: For 'floor' smoothing, the floor value to use. :param use_effective_order: Account for references that are shorter than the largest n-gram. :return: Returns a single BLEU score as a float. """ bleu = corpus_bleu( hypothesis, references, smooth_method=smooth_method, smooth_value=smooth_value, use_effective_order=use_effective_order, ) return bleu def corpus_bleu( sys_stream: Union[str, Iterable[str]], ref_streams: Union[str, List[Iterable[str]]], smooth_method="exp", smooth_value=SMOOTH_VALUE_DEFAULT, force=False, lowercase=False, tokenize=DEFAULT_TOKENIZER, use_effective_order=False, ) -> BLEU: """Produces BLEU scores along with its sufficient statistics from a source against one or more references. :param sys_stream: The system stream (a sequence of segments) :param ref_streams: A list of one or more reference streams (each a sequence of segments) :param smooth: The smoothing method to use :param smooth_value: For 'floor' smoothing, the floor to use :param force: Ignore data that looks already tokenized :param lowercase: Lowercase the data :param tokenize: The tokenizer to use :return: a BLEU object containing everything you'd want """ # Add some robustness to the input arguments if isinstance(sys_stream, str): sys_stream = [sys_stream] if isinstance(ref_streams, str): ref_streams = [[ref_streams]] sys_len = 0 ref_len = 0 correct = [0 for n in range(NGRAM_ORDER)] total = [0 for n in range(NGRAM_ORDER)] # look for already-tokenized sentences tokenized_count = 0 fhs = [sys_stream] + ref_streams for lines in zip_longest(*fhs): if None in lines: raise EOFError("Source and reference streams have different lengths!") if lowercase: lines = [x.lower() for x in lines] if not (force or tokenize == "none") and lines[0].rstrip().endswith(" ."): tokenized_count += 1 if tokenized_count == 100: logging.warning("That's 100 lines that end in a tokenized period ('.')") logging.warning( "It looks like you forgot to detokenize your test data, which may hurt your score." ) logging.warning( "If you insist your data is detokenized, or don't care, you can suppress this message with '--force'." ) output, *refs = [TOKENIZERS[tokenize](x.rstrip()) for x in lines] ref_ngrams, closest_diff, closest_len = ref_stats(output, refs) sys_len += len(output.split()) ref_len += closest_len sys_ngrams = extract_ngrams(output) for ngram in sys_ngrams.keys(): n = len(ngram.split()) correct[n - 1] += min(sys_ngrams[ngram], ref_ngrams.get(ngram, 0)) total[n - 1] += sys_ngrams[ngram] return compute_bleu( correct, total, sys_len, ref_len, smooth_method=smooth_method, smooth_value=smooth_value, use_effective_order=use_effective_order, ) def raw_corpus_bleu(sys_stream, ref_streams, smooth_value=SMOOTH_VALUE_DEFAULT) -> BLEU: """Convenience function that wraps corpus_bleu(). This is convenient if you're using sacrebleu as a library, say for scoring on dev. It uses no tokenization and 'floor' smoothing, with the floor default to 0 (no smoothing). :param sys_stream: the system stream (a sequence of segments) :param ref_streams: a list of one or more reference streams (each a sequence of segments) """ return corpus_bleu( sys_stream, ref_streams, smooth_method="floor", smooth_value=smooth_value, force=True, tokenize="none", use_effective_order=True, ) def delete_whitespace(text: str) -> str: """ Removes whitespaces from text. """ return re.sub(r"\s+", "", text).strip() def get_sentence_statistics( hypothesis: str, reference: str, order: int = CHRF_ORDER, remove_whitespace: bool = True, ) -> List[float]: hypothesis = delete_whitespace(hypothesis) if remove_whitespace else hypothesis reference = delete_whitespace(reference) if remove_whitespace else reference statistics = [0] * (order * 3) for i in range(order): n = i + 1 hypothesis_ngrams = extract_char_ngrams(hypothesis, n) reference_ngrams = extract_char_ngrams(reference, n) common_ngrams = hypothesis_ngrams & reference_ngrams statistics[3 * i + 0] = sum(hypothesis_ngrams.values()) statistics[3 * i + 1] = sum(reference_ngrams.values()) statistics[3 * i + 2] = sum(common_ngrams.values()) return statistics def get_corpus_statistics( hypotheses: Iterable[str], references: Iterable[str], order: int = CHRF_ORDER, remove_whitespace: bool = True, ) -> List[float]: corpus_statistics = [0] * (order * 3) for hypothesis, reference in zip(hypotheses, references): statistics = get_sentence_statistics( hypothesis, reference, order=order, remove_whitespace=remove_whitespace ) for i in range(len(statistics)): corpus_statistics[i] += statistics[i] return corpus_statistics def _avg_precision_and_recall( statistics: List[float], order: int ) -> Tuple[float, float]: avg_precision = 0.0 avg_recall = 0.0 effective_order = 0 for i in range(order): hypotheses_ngrams = statistics[3 * i + 0] references_ngrams = statistics[3 * i + 1] common_ngrams = statistics[3 * i + 2] if hypotheses_ngrams > 0 and references_ngrams > 0: avg_precision += common_ngrams / hypotheses_ngrams avg_recall += common_ngrams / references_ngrams effective_order += 1 if effective_order == 0: return 0.0, 0.0 avg_precision /= effective_order avg_recall /= effective_order return avg_precision, avg_recall def _chrf(avg_precision, avg_recall, beta: int = CHRF_BETA) -> float: if avg_precision + avg_recall == 0: return 0.0 beta_square = beta ** 2 score = ( (1 + beta_square) * (avg_precision * avg_recall) / ((beta_square * avg_precision) + avg_recall) ) return score def corpus_chrf( hypotheses: Iterable[str], references: Iterable[str], order: int = CHRF_ORDER, beta: float = CHRF_BETA, remove_whitespace: bool = True, ) -> CHRF: """ Computes Chrf on a corpus. :param hypotheses: Stream of hypotheses. :param references: Stream of references :param order: Maximum n-gram order. :param remove_whitespace: Whether to delete all whitespace from hypothesis and reference strings. :param beta: Defines importance of recall w.r.t precision. If beta=1, same importance. :return: Chrf score. """ corpus_statistics = get_corpus_statistics( hypotheses, references, order=order, remove_whitespace=remove_whitespace ) avg_precision, avg_recall = _avg_precision_and_recall(corpus_statistics, order) return CHRF(_chrf(avg_precision, avg_recall, beta=beta)) def sentence_chrf( hypothesis: str, reference: str, order: int = CHRF_ORDER, beta: float = CHRF_BETA, remove_whitespace: bool = True, ) -> CHRF: """ Computes ChrF on a single sentence pair. :param hypothesis: Hypothesis string. :param reference: Reference string. :param order: Maximum n-gram order. :param remove_whitespace: Whether to delete whitespaces from hypothesis and reference strings. :param beta: Defines importance of recall w.r.t precision. If beta=1, same importance. :return: Chrf score. """ statistics = get_sentence_statistics( hypothesis, reference, order=order, remove_whitespace=remove_whitespace ) avg_precision, avg_recall = _avg_precision_and_recall(statistics, order) return CHRF(_chrf(avg_precision, avg_recall, beta=beta)) def get_a_list_of_testset_names(): """Return a string with a formatted list of available test sets plus their descriptions. """ message = "The available test sets are:" for testset in sorted(DATASETS.keys(), reverse=True): message += "\n%20s: %s" % (testset, DATASETS[testset].get("description", "")) return message def _available_origlangs(test_sets, langpair): """Return a list of origlang values in according to the raw SGM files.""" origlangs = set() for test_set in test_sets.split(","): rawfile = os.path.join( SACREBLEU_DIR, test_set, "raw", DATASETS[test_set][langpair][0] ) if rawfile.endswith(".sgm"): with smart_open(rawfile) as fin: for line in fin: if line.startswith(" 1: logging.error("Only one metric can be used with Sentence-level reporting.") sys.exit(1) if args.citation: if not args.test_set: logging.error("I need a test set (-t).") sys.exit(1) for test_set in args.test_set.split(","): if "citation" not in DATASETS[test_set]: logging.error("No citation found for %s", test_set) else: print(DATASETS[test_set]["citation"]) sys.exit(0) if args.num_refs != 1 and (args.test_set is not None or len(args.refs) > 1): logging.error( "The --num-refs argument allows you to provide any number of tab-delimited references in a single file." ) logging.error( "You can only use it with externaly-provided references, however (i.e., not with `-t`)," ) logging.error("and you cannot then provide multiple reference files.") sys.exit(1) if args.test_set is not None: for test_set in args.test_set.split(","): if test_set not in DATASETS: logging.error( 'Unknown test set "%s"\n%s', test_set, get_a_list_of_testset_names() ) sys.exit(1) if args.test_set is None: if len(args.refs) == 0: logging.error( "I need either a predefined test set (-t) or a list of references" ) logging.error(get_a_list_of_testset_names()) sys.exit(1) elif len(args.refs) > 0: logging.error( "I need exactly one of (a) a predefined test set (-t) or (b) a list of references" ) sys.exit(1) elif args.langpair is None: logging.error("I need a language pair (-l).") sys.exit(1) else: for test_set in args.test_set.split(","): if args.langpair not in DATASETS[test_set]: logging.error('No such language pair "%s"', args.langpair) logging.error( 'Available language pairs for test set "%s": %s', test_set, ", ".join(x for x in DATASETS[test_set].keys() if "-" in x), ) sys.exit(1) if args.echo: if args.langpair is None or args.test_set is None: logging.warning("--echo requires a test set (--t) and a language pair (-l)") sys.exit(1) for test_set in args.test_set.split(","): print_test_set( test_set, args.langpair, args.echo, args.origlang, args.subset ) sys.exit(0) if args.test_set is not None and args.tokenize == "none": logging.warning( "You are turning off sacrebleu's internal tokenization ('--tokenize none'), presumably to supply\n" "your own reference tokenization. Published numbers will not be comparable with other papers.\n" ) # Internal tokenizer settings. Set to 'zh' for Chinese DEFAULT_TOKENIZER ( if args.tokenize is None: # set default if args.langpair is not None and args.langpair.split("-")[1] == "zh": args.tokenize = "zh" else: args.tokenize = DEFAULT_TOKENIZER if ( args.langpair is not None and args.langpair.split("-")[1] == "zh" and "bleu" in args.metrics and args.tokenize != "zh" ): logging.warning('You should also pass "--tok zh" when scoring Chinese...') # concat_ref_files is a list of list of reference filenames, for example: # concat_ref_files = [[testset1_refA, testset1_refB], [testset2_refA, testset2_refB]] if args.test_set is None: concat_ref_files = [args.refs] else: concat_ref_files = [] for test_set in args.test_set.split(","): _, *ref_files = download_test_set(test_set, args.langpair) if len(ref_files) == 0: logging.warning( "No references found for test set {}/{}.".format( test_set, args.langpair ) ) concat_ref_files.append(ref_files) inputfh = ( io.TextIOWrapper(sys.stdin.buffer, encoding=args.encoding) if args.input == "-" else smart_open(args.input, encoding=args.encoding) ) full_system = inputfh.readlines() # Read references full_refs = [[] for x in range(max(len(concat_ref_files[0]), args.num_refs))] for ref_files in concat_ref_files: for refno, ref_file in enumerate(ref_files): for lineno, line in enumerate( smart_open(ref_file, encoding=args.encoding), 1 ): if args.num_refs != 1: splits = line.rstrip().split(sep="\t", maxsplit=args.num_refs - 1) if len(splits) != args.num_refs: logging.error( "FATAL: line {}: expected {} fields, but found {}.".format( lineno, args.num_refs, len(splits) ) ) sys.exit(17) for refno, split in enumerate(splits): full_refs[refno].append(split) else: full_refs[refno].append(line) # Filter sentences according to a given origlang system, *refs = _filter_subset( [full_system, *full_refs], args.test_set, args.langpair, args.origlang, args.subset, ) if len(system) == 0: message = "Test set %s contains no sentence" % args.test_set if args.origlang is not None or args.subset is not None: message += " with" message += "" if args.origlang is None else " origlang=" + args.origlang message += "" if args.subset is None else " subset=" + args.subset logging.error(message) exit(1) # Handle sentence level and quit if args.sentence_level: for output, *references in zip(system, *refs): results = [] for metric in args.metrics: if metric == "bleu": bleu = sentence_bleu( output, [[x] for x in references], smooth_method=args.smooth, smooth_value=args.smooth_value, ) results.append(bleu) if metric == "chrf": chrf = sentence_chrf( output, references[0], args.chrf_order, args.chrf_beta, remove_whitespace=not args.chrf_whitespace, ) results.append(chrf) display_metric(args.metrics, results, len(refs), args) sys.exit(0) # Else, handle system level results = [] try: for metric in args.metrics: if metric == "bleu": bleu = corpus_bleu( system, refs, smooth_method=args.smooth, smooth_value=args.smooth_value, force=args.force, lowercase=args.lc, tokenize=args.tokenize, ) results.append(bleu) elif metric == "chrf": chrf = corpus_chrf( system, refs[0], beta=args.chrf_beta, order=args.chrf_order, remove_whitespace=not args.chrf_whitespace, ) results.append(chrf) except EOFError: logging.error("The input and reference stream(s) were of different lengths.") if args.test_set is not None: logging.error( "\nThis could be a problem with your system output or with sacreBLEU's reference database.\n" "If the latter, you can clean out the references cache by typing:\n" "\n" " rm -r %s/%s\n" "\n" "They will be downloaded automatically again the next time you run sacreBLEU.", SACREBLEU_DIR, args.test_set, ) sys.exit(1) display_metric(args.metrics, results, len(refs), args) if args.detail: width = args.width sents_digits = len(str(len(full_system))) origlangs = ( args.origlang if args.origlang else _available_origlangs(args.test_set, args.langpair) ) for origlang in origlangs: subsets = [None] if args.subset is not None: subsets += [args.subset] elif all(t in SUBSETS for t in args.test_set.split(",")): subsets += COUNTRIES + DOMAINS for subset in subsets: system, *refs = _filter_subset( [full_system, *full_refs], args.test_set, args.langpair, origlang, subset, ) if len(system) == 0: continue if subset in COUNTRIES: subset_str = "%20s" % ("country=" + subset) elif subset in DOMAINS: subset_str = "%20s" % ("domain=" + subset) else: subset_str = "%20s" % "" if "bleu" in args.metrics: bleu = corpus_bleu( system, refs, smooth_method=args.smooth, smooth_value=args.smooth_value, force=args.force, lowercase=args.lc, tokenize=args.tokenize, ) print( "origlang={} {}: sentences={:{}} BLEU={:{}.{}f}".format( origlang, subset_str, len(system), sents_digits, bleu.score, width + 4, width, ) ) if "chrf" in args.metrics: chrf = corpus_chrf( system, refs[0], beta=args.chrf_beta, order=args.chrf_order, remove_whitespace=not args.chrf_whitespace, ) print( "origlang={} {}: sentences={:{}} chrF={:{}.{}f}".format( origlang, subset_str, len(system), sents_digits, chrf.score, width + 4, width, ) ) def display_metric(metrics_to_print, results, num_refs, args): """ Badly in need of refactoring. One idea is to put all of this in the BLEU and CHRF classes, and then define a Result::signature() function. """ for metric, result in zip(metrics_to_print, results): if metric == "bleu": if args.score_only: print("{0:.{1}f}".format(result.score, args.width)) else: version_str = bleu_signature(args, num_refs) print(result.format(args.width).replace("BLEU", "BLEU+" + version_str)) elif metric == "chrf": if args.score_only: print("{0:.{1}f}".format(result.score, args.width)) else: version_str = chrf_signature(args, num_refs) print( "chrF{0:d}+{1} = {2:.{3}f}".format( args.chrf_beta, version_str, result.score, args.width ) ) if __name__ == "__main__": main()