Chinese Couplet Generation with Syntactic Information

Yan Song


Abstract
Chinese couplet generation aims to generate a pair of clauses (usually generating a subsequent clause given an antecedent one) with certain rules (e.g., morphological and syntactical symmetry) adhered and has long been a challenging task with cultural background. To generate high-quality couplet (antecedent) clauses, it normally requires a model to learn the correspondences between antecedent and subsequent clauses under aforementioned rules and constraint of few characters with their concise usage. To tackle this task, previous studies normally directly adopt deep neural networks without explicitly taking into account fine-grained analysis of the clauses, in this paper, we propose to enhance Chinese couplet generation by leveraging syntactic information, i.e., part-of-speech (POS) tags and word dependencies. In doing so, we identify word boundaries in the antecedent clause and then use a special attention module to encode the syntactic information over the words for better generating the subsequent clause. Experimental results on a dataset for Chinese couplet generation illustrate the validity and effectiveness of our approach, which outperforms strong baselines with respect to automatic and manual evaluation metrics.
Anthology ID:
2022.coling-1.560
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6436–6446
Language:
URL:
https://aclanthology.org/2022.coling-1.560
DOI:
Bibkey:
Cite (ACL):
Yan Song. 2022. Chinese Couplet Generation with Syntactic Information. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6436–6446, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Chinese Couplet Generation with Syntactic Information (Song, COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.560.pdf