Going Beyond Word Matching: Syntax Improves In-context Example Selection
  for Machine Translation
Chenming Tang Zhixiang Wang Yunfang Wu
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.