Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.18653/v1/2022.acl-long.133 | PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022), VOL 1: (LONG PAPERS) |
DocType | Volume | Citations |
Conference | Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minghuan Tan | 1 | 0 | 2.03 |
| Yong Dai | 2 | 0 | 0.34 |
| Duyu Tang | 3 | 0 | 0.68 |
| Zhangyin Feng | 4 | 0 | 1.69 |
| Guoping Huang | 5 | 0 | 0.34 |
| Jing Jiang | 6 | 0 | 0.34 |
| Jiwei Li | 7 | 0 | 0.34 |
| Shuming Shi | 8 | 620 | 58.27 |