cushLEPOR: customising hLEPOR metric using Optuna for higher agreement with human judgments or pre-trained language model LaBSE
This work addresses the high cost and trust issues in human evaluation for machine translation researchers, though it is incremental as it builds on existing metrics.
The paper tackles the problem of unreliable automatic metrics for machine translation by customizing the hLEPOR metric using Optuna to better align with human judgments and pre-trained language models like LaBSE, resulting in improved performance over BLEU and wins in three language pairs including English-German and Chinese-English.
Human evaluation has always been expensive while researchers struggle to trust the automatic metrics. To address this, we propose to customise traditional metrics by taking advantages of the pre-trained language models (PLMs) and the limited available human labelled scores. We first re-introduce the hLEPOR metric factors, followed by the Python version we developed (ported) which achieved the automatic tuning of the weighting parameters in hLEPOR metric. Then we present the customised hLEPOR (cushLEPOR) which uses Optuna hyper-parameter optimisation framework to fine-tune hLEPOR weighting parameters towards better agreement to pre-trained language models (using LaBSE) regarding the exact MT language pairs that cushLEPOR is deployed to. We also optimise cushLEPOR towards professional human evaluation data based on MQM and pSQM framework on English-German and Chinese-English language pairs. The experimental investigations show cushLEPOR boosts hLEPOR performances towards better agreements to PLMs like LaBSE with much lower cost, and better agreements to human evaluations including MQM and pSQM scores, and yields much better performances than BLEU (data available at \url{https://github.com/poethan/cushLEPOR}). Official results show that our submissions win three language pairs including \textbf{English-German} and \textbf{Chinese-English} on \textit{News} domain via cushLEPOR(LM) and \textbf{English-Russian} on \textit{TED} domain via hLEPOR.