Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
12-2020
Abstract
Chinese idioms are special fixed phrases usually derived from ancient stories, whose meanings are oftentimes highly idiomatic and non-compositional. The Chinese idiom prediction task is to select the correct idiom from a set of candidate idioms given a context with a blank. We propose a BERT-based dual embedding model to encode the contextual words as well as to learn dual embeddings of the idioms. Specifically, we first match the embedding of each candidate idiom with the hidden representation corresponding to the blank in the context. We then match the embedding of each candidate idiom with the hidden representations of all the tokens in the context thorough context pooling. We further propose to use two separate idiom embeddings for the two kinds of matching. Experiments on a recently released Chinese idiom cloze test dataset show that our proposed method performs better than the existing state of the art. Ablation experiments also show that both context pooling and dual embedding contribute to the improvement of performance.
Discipline
Databases and Information Systems | Programming Languages and Compilers
Research Areas
Data Science and Engineering
Publication
Proceedings of the 28th International Conference on Computational Linguistics, Virtual Conference, 2020 December 8-13
First Page
1312
Last Page
1322
ISBN
9781952148279
Publisher
Association for Computational Linguistics
City or Country
Barcelona, Spain (Online)
Citation
TAN, Minghuan and JIANG, Jing.
A BERT-based dual embedding model for Chinese idiom prediction. (2020). Proceedings of the 28th International Conference on Computational Linguistics, Virtual Conference, 2020 December 8-13. 1312-1322.
Available at: https://ink.library.smu.edu.sg/sis_research/5605
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.