Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2022

Abstract

While GPT has become the de-facto method for text generation tasks, its application to pinyin input method remains unexplored. In this work, we make the first exploration to leverage Chinese GPT for pinyin input method. We find that a frozen GPT achieves state-of-the-art performance on perfect pinyin. However, the performance drops dramatically when the input includes abbreviated pinyin. A reason is that an abbreviated pinyin can be mapped to many perfect pinyin, which links to even larger number of Chinese characters. We mitigate this issue with two strategies, including enriching the context with pinyin and optimizing the training process to help distinguish homophones. To further facilitate the evaluation of pinyin input method, we create a dataset consisting of 270K instances from fifteen domains. Results show that our approach improves the performance on abbreviated pinyin across all domains. Model analysis demonstrates that both strategies contribute to the performance boost.

Keywords

Chinese characters, Input methods, ITS applications, Modeling analyzes, training process

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems | Programming Languages and Compilers

Research Areas

Data Science and Engineering

Publication

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland, 2022 May 22-27

First Page

1899

Last Page

1909

Identifier

10.18653/v1/2022.acl-long.133

Publisher

ACL

City or Country

Dublin

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Additional URL

https://doi.org/10.18653/v1/2022.acl-long.133

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