QE-fusion

Don't Rank, Combine! Combining Machine Translation Hypotheses Using Quality Estimation
Giorgos Vernikos, Andrei Popescu-Belis

Overview

QE-fusion⚛ is a novel approach that uses Quality Estimation (QE) metrics to improve translations by fusing different translation candidates. QE-fusion leverages the potential complementarity of a candidate pool by spotting variations among candidates and using a QE metric to select the spans that boost overall translation quality.

This repository contains code for sampling translations from an LLM or an MT model, implementing QE-fusion, and other reranking approaches, and scoring the corresponding outputs.

Installation

This project requires Python 3.10, PyTorch 1.13.1, and transformers 4.34.0.

It's advisable to set up a separate environment for this project and install the necessary dependencies:

conda create -n qe-fusion python=3.10
conda activate qe-fusion
pip install -r requirements.txt

Obtain Translation Hypotheses

The script assumes that the test set of each language pair (can be downloaded from sacreBLEU) is located in the folder data/<s>-<t>/ under the names test.<s> and test.<t> where <s> and <t> are the source and target languages.

The first step is to obtain multiple translation hypotheses for each input. To do so, we either use an LLM via few-shot (8-shot) learning or an MT model from which we sample k outputs using the llm_query.py script.

python llm_query.py --model llama2-7b --lp en-de --bsize 4 --decoding_alg sample --sample 5 --temperature 0.6 --exemplars 8 

This script also supports greedy decoding and beam search via the decoding_alg parameter.

Fusion of Hypotheses

To fuse the generated hypotheses use the select_outputs.py script:

python select_outputs.py --model llama2-7b --lp en-de --generation sample-t0.6 --cands_pool 5 --criterion cometqe --method fusion 

To implement reranking approaches such as QE-reranking and MBR modify the --criterion and --method parameters accordingly.

Evaluation

To evaluate the quality of the produced translations use the score_outs.py script:

python score_outs.py --model llama2-7b --lp en-de --cands_pool 5 --generation sample-t0.6 --criterion cometqe/cometqe-fusion-beam5-kbest0  --metrics ['bleu', 'chrf', 'comet', 'bleurt']

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Reference

Please feel free to cite our paper if you use our code or proposed algorithm.:

@misc{vernikos2024dont,
      title={Don't Rank, Combine! Combining Machine Translation Hypotheses Using Quality Estimation}, 
      author={Giorgos Vernikos and Andrei Popescu-Belis},
      year={2024},
      eprint={2401.06688},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

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Contact

Please feel free to raise an issue or contact me in case you require any help setting up the repo!