Going Beyond Word Matching: Syntax Improves In-context Example Selection for Machine Translation
<|reference_start|>Going Beyond Word Matching: Syntax Improves In-context Example Selection for Machine Translation: 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.<|reference_end|>
@article{tang2024going,
    title={Going Beyond Word Matching: Syntax Improves In-context Example Selection
  for Machine Translation},
    author={Chenming Tang, Zhixiang Wang and Yunfang Wu},
    journal={arXiv preprint arXiv:2403.19285},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19285},
    primaryClass={cs.CL}
}