CTQScorer: Combining Multiple Features for In-context Example Selection for Machine Translation

Aswanth Kumar M; Ratish Puduppully; Raj Dabre; Anoop Kunchukuttan

EMNLP 2023long findings0 citations

CTQScorer: Combining Multiple Features for In-context Example Selection for Machine Translation

Aswanth Kumar M, Ratish Puduppully, Raj Dabre, Anoop Kunchukuttan

Abstract

Large language models have demonstrated the capability to perform on machine translation when the input is prompted with a few examples (in-context learning). Translation quality depends on various features of the selected examples, such as their quality and relevance, but previous work has predominantly focused on individual features in isolation. In this paper, we propose a general framework for combining different features influencing example selection. We learn a regression model, CTQ Scorer (Contextual Translation Quality), that selects examples based on multiple features in order to maximize the translation quality. On multiple language pairs and language models, we show that CTQ Scorer helps significantly outperform random selection as well as strong single-factor baselines reported in the literature. We also see an improvement of over 2.5 COMET points on average with respect to a strong BM25 retrieval-based baseline.

few-shot promptingmachine translationexample selection

BibTeX

@inproceedings{
m2023ctqscorer,
title={{CTQS}corer: Combining Multiple Features for In-context Example Selection for Machine Translation},
author={Aswanth Kumar M and Ratish Puduppully and Raj Dabre and Anoop Kunchukuttan},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=WQR3xpEJRJ}
}