Summary of Efficient Technical Term Translation: a Knowledge Distillation Approach For Parenthetical Terminology Translation, by Jiyoon Myung et al.
Efficient Technical Term Translation: A Knowledge Distillation Approach for Parenthetical Terminology Translation
by Jiyoon Myung, Jihyeon Park, Jungki Son, Kyungro Lee, Joohyung Han
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Parenthetical Terminology Translation (PTT) task aims to improve the accuracy of translating technical terms by displaying the original term in parentheses alongside its translation. To implement this approach, a representative PTT dataset was generated using a collaborative effort between large language models and traditional Neural Machine Translation (NMT) models were fine-tuned through knowledge distillation. A novel evaluation metric was also developed to assess both overall translation accuracy and the correct parenthetical presentation of terms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new approach to translating technical terms by showing the original term in parentheses alongside its translation. This helps make sure that the translated text is accurate and clear. The researchers created a special dataset for this task and used it to fine-tune traditional language models and smaller models called small-sized Large Language Models (sLMs). They also came up with a new way to measure how well the translations work. |
Keywords
» Artificial intelligence » Knowledge distillation » Translation
