COMPILING: A Benchmark Dataset for Chinese Complexity Controllable Definition Generation

Yuan Jiaxin, Kong Cunliang, Xie Chenhui, Yang Liner, Yang Erhong

Abstract
“The definition generation task aims to generate a word’s definition within a specific context automatically. However, owing to the lack of datasets for different complexities, the definitions produced by models tend to keep the same complexity level. This paper proposes a novel task of generating definitions for a word with controllable complexity levels. Correspondingly, we introduce COMPILING, a dataset given detailed information about Chinese definitions, and each definition is labeled with its complexity levels. The COMPILING dataset includes 74,303 words and 106,882 definitions. To the best of our knowledge, it is the largest dataset of the Chinese definition generation task. We select various representative generation methods as baselines for this task and conduct evaluations, which illustrates that our dataset plays an outstanding role in assisting models in generating different complexity-level definitions. We believe that the COMPILING dataset will benefit further research in complexity controllable definition generation.”
Anthology ID:
2022.ccl-1.81
Volume:
Proceedings of the 21st Chinese National Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Nanchang, China
Venue:
CCL
SIG:
Publisher:
Chinese Information Processing Society of China
Note:
Pages:
921–931
Language:
English
URL:
https://aclanthology.org/2022.ccl-1.81
DOI:
Bibkey:
Cite (ACL):
Yuan Jiaxin, Kong Cunliang, Xie Chenhui, Yang Liner, and Yang Erhong. 2022. COMPILING: A Benchmark Dataset for Chinese Complexity Controllable Definition Generation. In Proceedings of the 21st Chinese National Conference on Computational Linguistics, pages 921–931, Nanchang, China. Chinese Information Processing Society of China.
Cite (Informal):
COMPILING: A Benchmark Dataset for Chinese Complexity Controllable Definition Generation (Jiaxin et al., CCL 2022)
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https://preview.aclanthology.org/ingestion-script-update/2022.ccl-1.81.pdf
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