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arXiv · 2403.19285

Going Beyond Word Matching: Syntax Improves In-context Example Selection for Machine Translation

Chenming Tang·Zhixiang Wang·Yunfang Wu
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Abstract

In-context learning (ICL) is the trending prompting strategy in the era of large language models (LLMs), where a few examples are demonstrated to evoke LLMs' power for a given task. How to select informative examples remains an open issue. Previous works on in-context example selection for machine translation (MT) focus on superficial word-level features while ignoring deep syntax-level knowledge. In this paper, we propose a syntax-based in-context example selection method for MT, by computing the syntactic similarity between dependency trees using Polynomial Distance. In addition, we propose an ensemble strategy combining examples selected by both word-level and syntax-level criteria. Experimental results between English and 6 common languages indicate that syntax can effectively enhancing ICL for MT, obtaining the highest COMET scores on 11 out of 12 translation directions.

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Chenming Tang, Zhixiang Wang, Yunfang Wu. 2024-03-28. Going Beyond Word Matching: Syntax Improves In-context Example Selection for Machine Translation. https://arxiv.org/abs/2403.19285

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