Datasets:
File size: 2,031 Bytes
7472219 c24e4b4 7472219 c24e4b4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | ---
license: cc-by-4.0
language:
- zh
pretty_name: CSRTE-Text
tags:
- text-relation-extraction
- relation-extraction
- chinese
---
# CSRTE-Text
CSRTE-Text is the text-only counterpart of the CSRTE Chinese relation extraction benchmark. It contains transcriptions with named-entity and relation annotations, and is intended for text-only SpeechRE experiments, analysis, and comparison with the audio-based benchmark.
## Data format
The dataset is provided in `train.json`, `dev.json`, and `test.json`. Each JSON record contains:
- `sentence`: the transcription of the utterance.
- `audio_id`: the identifier inherited from the paired speech record.
- `data_annotation`: entity spans and relation annotations. Entity spans use `start`, `end`, `text`, and `labels`; relations identify their source and target entities and relation labels.
- `data_info`: available metadata associated with the utterance.
`dataset_info.json` lists the entity and relation label sets used by CSRTE.
## License
The dataset is released under the [Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/). Please check the licenses and terms of the underlying sources before redistribution or commercial use.
## Citation
CSRTE was introduced and used by the following work:
```bibtex
@inproceedings{mu-etal-2026-acre,
title = "{ACRE}: An Acoustic-Cue and Contextual-Knowledge Reasoning Enhancement Framework for Speech Relation Extraction",
author = "Mu, Wenxuan and
Ning, Jinzhong and
Zhao, Di and
Zhang, Yijia",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1244/",
doi = "10.18653/2026.findings-emnlp.1244",
pages = "26343--26352",
ISBN = "979-8-89176-462-0"
}
```
|