@inproceedings{huang-etal-2021-definition,
title = "Definition Modelling for Appropriate Specificity",
author = "Huang, Han and
Kajiwara, Tomoyuki and
Arase, Yuki",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.194",
doi = "10.18653/v1/2021.emnlp-main.194",
pages = "2499--2509",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Definition Modelling for Appropriate Specificity
%A Huang, Han
%A Kajiwara, Tomoyuki
%A Arase, Yuki
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F huang-etal-2021-definition
%X 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.
%R 10.18653/v1/2021.emnlp-main.194
%U https://aclanthology.org/2021.emnlp-main.194
%U https://doi.org/10.18653/v1/2021.emnlp-main.194
%P 2499-2509
Markdown (Informal)
[Definition Modelling for Appropriate Specificity](https://aclanthology.org/2021.emnlp-main.194) (Huang et al., EMNLP 2021)
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.