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morphbench-task6-final / README.md
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README: 09-17 en/de/fr numbers + tr config merged (fixes the --lang-all revert)
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metadata 
dataset_info:
  - config_name: de
    features:
      - name: word
        dtype: string
      - name: gloss
        dtype: string
      - name: gold_glosses
        sequence: string
      - name: base
        dtype: string
      - name: affix
        sequence: string
      - name: function
        dtype: string
      - name: derived_freq
        dtype: int64
      - name: base_exposure
        dtype: int64
      - name: status
        dtype: string
      - name: dictcc_tags
        sequence: string
      - name: segmentation
        dtype: string
    splits:
      - name: train
        num_bytes: 2202109
        num_examples: 8709
      - name: dev
        num_bytes: 288766
        num_examples: 1015
      - name: test_unseen_basehigh
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        num_examples: 376
      - name: test_unseen_baselow
        num_bytes: 144077
        num_examples: 526
      - name: test_seen_basehigh
        num_bytes: 268386
        num_examples: 882
      - name: test_seen_baselow
        num_bytes: 134996
        num_examples: 434
    download_size: 1636638
    dataset_size: 3145011
  - config_name: en
    features:
      - name: word
        dtype: string
      - name: gloss
        dtype: string
      - name: gold_glosses
        sequence: string
      - name: base
        dtype: string
      - name: affix
        sequence: string
      - name: function
        dtype: string
      - name: derived_freq
        dtype: int64
      - name: base_exposure
        dtype: int64
      - name: status
        dtype: string
      - name: dictcc_tags
        sequence: string
      - name: wiktionary_tags
        sequence: string
      - name: segmentation
        dtype: string
    splits:
      - name: train
        num_bytes: 3169003
        num_examples: 13715
      - name: dev
        num_bytes: 533765
        num_examples: 1892
      - name: test_unseen_basehigh
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        num_examples: 1494
      - name: test_unseen_baselow
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        num_examples: 1508
      - name: test_seen_basehigh
        num_bytes: 235910
        num_examples: 605
      - name: test_seen_baselow
        num_bytes: 120271
        num_examples: 295
    download_size: 2313761
    dataset_size: 4863147
  - config_name: fr
    features:
      - name: word
        dtype: string
      - name: gloss
        dtype: string
      - name: gold_glosses
        list: string
      - name: base
        dtype: string
      - name: affix
        dtype: string
      - name: function
        dtype: string
      - name: derived_freq
        dtype: int64
      - name: base_exposure
        dtype: int64
      - name: status
        dtype: string
    splits:
      - name: train
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      - name: dev
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        num_examples: 1475
      - name: test_unseen_basehigh
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        num_examples: 384
      - name: test_unseen_baselow
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        num_examples: 646
      - name: test_seen_basehigh
        num_bytes: 301375
        num_examples: 1114
      - name: test_seen_baselow
        num_bytes: 223518
        num_examples: 873
    download_size: 2010995
    dataset_size: 3172716
  - config_name: tr
    features:
      - name: word
        dtype: string
      - name: gloss
        dtype: string
      - name: gold_glosses
        sequence: string
      - name: base
        dtype: string
      - name: affix
        dtype: string
      - name: function
        dtype: string
      - name: derived_freq
        dtype: int64
      - name: base_exposure
        dtype: int64
      - name: status
        dtype: string
    splits:
      - name: train
        num_bytes: 848753
        num_examples: 5523
      - name: dev
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        num_examples: 674
      - name: test_unseen_basehigh
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        num_examples: 403
      - name: test_unseen_baselow
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        num_examples: 172
      - name: test_seen_basehigh
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        num_examples: 824
      - name: test_seen_baselow
        num_bytes: 14233
        num_examples: 84
    download_size: 664315
    dataset_size: 1239603
configs:
  - config_name: de
    data_files:
      - split: train
        path: de/train-*
      - split: dev
        path: de/dev-*
      - split: test_unseen_basehigh
        path: de/test_unseen_basehigh-*
      - split: test_unseen_baselow
        path: de/test_unseen_baselow-*
      - split: test_seen_basehigh
        path: de/test_seen_basehigh-*
      - split: test_seen_baselow
        path: de/test_seen_baselow-*
  - config_name: en
    data_files:
      - split: train
        path: en/train-*
      - split: dev
        path: en/dev-*
      - split: test_unseen_basehigh
        path: en/test_unseen_basehigh-*
      - split: test_unseen_baselow
        path: en/test_unseen_baselow-*
      - split: test_seen_basehigh
        path: en/test_seen_basehigh-*
      - split: test_seen_baselow
        path: en/test_seen_baselow-*
  - config_name: fr
    data_files:
      - split: train
        path: fr/train-*
      - split: dev
        path: fr/dev-*
      - split: test_unseen_basehigh
        path: fr/test_unseen_basehigh-*
      - split: test_unseen_baselow
        path: fr/test_unseen_baselow-*
      - split: test_seen_basehigh
        path: fr/test_seen_basehigh-*
      - split: test_seen_baselow
        path: fr/test_seen_baselow-*
  - config_name: tr
    data_files:
      - split: train
        path: tr/train-*
      - split: dev
        path: tr/dev-*
      - split: test_unseen_basehigh
        path: tr/test_unseen_basehigh-*
      - split: test_unseen_baselow
        path: tr/test_unseen_baselow-*
      - split: test_seen_basehigh
        path: tr/test_seen_basehigh-*
      - split: test_seen_baselow
        path: tr/test_seen_baselow-*

MorphBench Task 6 — Complex-Word Definition (EN / DE / FR / TR)

Given a morphologically complex word, generate its dictionary definition:

define word=<word> ->   →   gloss

One config per language. Derivations come from UniMorph, glosses from the Wiktionary edition of that language.

from datasets import load_dataset
d = load_dataset("yuanxin112/morphbench-task6-final", "en")   # or "de" / "fr"
d["test_unseen_basehigh"][0]   # or "de" / "fr" / "tr"

Splits

| | EN | DE | FR | TR | |---|---:|---:|---:||---:| | train | 13,715 | 8,709 | 10,600 | 5,523 | | dev | 1,892 | 1,015 | 1,475 | 674 | | test_unseen_basehigh ← headline | 1,494 | 376 | 384 | 403 | | test_unseen_baselow | 1,508 | 526 | 646 | 172 | | test_seen_basehigh | 605 | 882 | 1,114 | 824 | | test_seen_baselow | 295 | 434 | 873 | 84 | | total | 19,509 | 11,942 | 15,092 | 7,680 |

Cell membership in EN/DE was re-tiered on 2026-09-17: base/affix now carry the deepest UniMorph decomposition (new segmentation column, affix is a list) and base_exposure was recomputed for the deeper roots, moving rows whose tier crossed the cut. FR is untouched. Results on the pre-2026-09-17 cells are not comparable to the new cell sizes. Splits are word-family disjoint — a union-find over the derivation graph puts every member of a family on the same side, so a model cannot be trained on one relative and tested on another.

The exposure grid

The test side crosses two axes, both measured on the corpus the evaluated models were actually pretrained on:

  • derived_freq — occurrences of the whole word. Case-folded, and counting its hyphenated spelling and its inflected forms, so unseen means the lexeme never appeared in any form. unseen = 0, seen = 1+.
  • base_exposure — substring exposure of the base over corpus types seen ≥ 5 times.

Headline = test_unseen_basehigh: the model has never seen the word but has seen its base, so it has to compose base + affix.

Per-language parameters

EN DE FR TR
pretraining corpus BabyLM babylm_strict_small (clean_v7) BabyLM babylm_deu_10m BabyLM babylm_fra_10m BabyLM babylm_tur_10m (official babylm-tur core + OPUS TED2020/QED + Wikipedia tr)
corpus tokens 9,299,870 9,909,390 10,734,109 9,640,149
base_exposure cut 50 53 58 52
  per million 5.38 5.35 5.40 5.39
wordfreq zipf floor on the base 2.0 1.0 1.5 1.5
affixes / function labels 60 / 32 154 / 40 144 / 30 26 / 26
eval rows with >1 reference 51% 37% 59% 30%
built by scripts/en/wiktionary/26_build_task6_definition.py scripts/deu/wiktionary/26_build_task6_definition_deu.py scripts/fra/wiktionary/26_build_task6_definition_fra.py scripts/tur/wiktionary/26_build_task6_definition_tur.py

Multi-reference scoring

gold_glosses holds up to 3 Wiktionary senses, with gold_glosses[0] == gloss, the single SFT target. Sense 1 is often not the sense a model would reasonably produce, so generation metrics should score against every reference and keep the best.

It is a scoring field, populated for dev and the four test splits only; on train it always holds just the target, in all four languages.

⚠️ Reference counts are not balanced across cells — the seen cells carry more senses, so the multi-reference lift is larger there. Report the single-reference number alongside, or the exposure contrast will look bigger than it is.

Licence

UniMorph is CC-BY-SA; Wiktionary text is CC-BY-SA 3.0, so this dataset inherits CC-BY-SA 4.0.