Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.05916 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917861501435904 |
|---|---|
| author | Chang, Ke-Ching Chen, Chung-Chi Yen, An-Zi |
| author_facet | Chang, Ke-Ching Chen, Chung-Chi Yen, An-Zi |
| contents | Large Language Models (LLMs) have demonstrated significant capabilities in machine translation. However, their translation quality is sometimes questioned, as the generated outputs may deviate from expressions typically used by native speakers. These deviations often arise from differences in sentence structure between language systems. To address this issue, we propose ParaAlign Translator, a method that fine-tunes LLMs to paraphrase sentences, aligning their structures with those of the target language systems. This approach improves the performance of subsequent translations. Experimental results demonstrate that the proposed method enhances the LLaMA-3-8B model's performance in both resource-rich and low-resource scenarios and achieves parity with or surpassing the much larger LLaMA-3-70B model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05916 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Paraphrase-Aligned Machine Translation Chang, Ke-Ching Chen, Chung-Chi Yen, An-Zi Computation and Language Large Language Models (LLMs) have demonstrated significant capabilities in machine translation. However, their translation quality is sometimes questioned, as the generated outputs may deviate from expressions typically used by native speakers. These deviations often arise from differences in sentence structure between language systems. To address this issue, we propose ParaAlign Translator, a method that fine-tunes LLMs to paraphrase sentences, aligning their structures with those of the target language systems. This approach improves the performance of subsequent translations. Experimental results demonstrate that the proposed method enhances the LLaMA-3-8B model's performance in both resource-rich and low-resource scenarios and achieves parity with or surpassing the much larger LLaMA-3-70B model. |
| title | Paraphrase-Aligned Machine Translation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.05916 |