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. Official results show that our submissions win three language pairs including English-German and Chinese-English on News domain via cushLEPOR(LM) and English-Russian on TED domain via hLEPOR. (data available at https://github.com/poethan/cushLEPOR)

Automatic MT evaluation metrics are indispensable for MT research. Augmented metrics such as hLEPOR include broader evaluation factors (recall and position difference penalty) in addition to the factors used in BLEU (sentence length, precision), and demonstrated higher accuracy. However, the obstacles preventing the wide use of hLEPOR were the lack of easy portable Python package and empirical weighting parameters that were tuned by manual work. This project addresses the above issues by offering a Python implementation of hLEPOR and automatic tuning of the parameters. We use existing translation memories (TM) as reference set and distillation modeling with LaBSE (Language-Agnostic BERT Sentence Embedding) to calibrate parameters for custom hLEPOR (cushLEPOR). cushLEPOR maximizes the correlation between hLEPOR and the distilling model similarity score towards reference. It can be used quickly and precisely to evaluate MT output from different engines, without need of manual weight tuning for optimization. In this session you will learn how to tune hLEPOR to obtain automatic custom-tuned cushLEPOR metric far more precise than BLEU. The method does not require costly human evaluations, existing TM is taken as a reference translation set, and cushLEPOR is created to select the best MT engine for the reference data-set.