Definition Modelling for Appropriate Specificity

Han Huang, Tomoyuki Kajiwara, Yuki Arase


Abstract
Definition generation techniques aim to generate a definition of a target word or phrase given a context. In previous studies, researchers have faced various issues such as the out-of-vocabulary problem and over/under-specificity problems. Over-specific definitions present narrow word meanings, whereas under-specific definitions present general and context-insensitive meanings. Herein, we propose a method for definition generation with appropriate specificity. The proposed method addresses the aforementioned problems by leveraging a pre-trained encoder-decoder model, namely Text-to-Text Transfer Transformer, and introducing a re-ranking mechanism to model specificity in definitions. Experimental results on standard evaluation datasets indicate that our method significantly outperforms the previous state-of-the-art method. Moreover, manual evaluation confirms that our method effectively addresses the over/under-specificity problems.
Anthology ID:
2021.emnlp-main.194
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2499–2509
Language:
URL:
https://aclanthology.org/2021.emnlp-main.194
DOI:
10.18653/v1/2021.emnlp-main.194
Bibkey:
Cite (ACL):
Han Huang, Tomoyuki Kajiwara, and Yuki Arase. 2021. Definition Modelling for Appropriate Specificity. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2499–2509, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Definition Modelling for Appropriate Specificity (Huang et al., EMNLP 2021)
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PDF:
https://aclanthology.org/2021.emnlp-main.194.pdf
Video:
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