Infosys Machine Translation System for WMT 20 Similar Language Translation Task (2020)

Abstract: This paper describes Infosys’s submission to the WMT20 Similar Language Translation shared task. We participated in Indo-Aryan language pair in the language direction Hindi to Marathi. Our baseline system is byte-pair encoding based transformer model trained with the Fairseq sequence modeling toolkit. Our final system is an ensemble of two transformer models, which ranked first in WMT20 evaluation. One model is designed to learn the nuances of translation of this low resource language pair by taking advantage of the fact that the source and target languages are same alphabet languages. The other model is the result of experimentation with the proportion of back-translated data to the parallel data to improve translation fluency.

## introduction neural machine translation @xcite is the most popular approach for machine translation. transformerbased nmt has outperformed many recurrent neural network based models. there is scope for improvement in nmt, particularly for low-resource language pairs. our techniques are experimented on the fairseq sequence modeling toolkit @xcite for nmt. our system is an ensemble of two transformer-based models. one designed for lowresource language pairs by taking advantage that both are same alphabet languages. the other model is built after experimenting on renowned backtranslation technique @xcite ) by exploiting target monolingual data. ## data hindi-marathi bitext data contains ∼49k sentence pairs. target monolingual data comprises of 326k newscrawl sentences and 10,839k raw sentences. ## system overview our hindi-marathi primary system is an ensemble of two transformer models. one is backtranslated model and the other model is trained on anonymized data. ## results our novel anonymization technique improved bleu by 1.2. optimal proportion of backtranslated data improved bleu by 3.5. ensembling best systems improved bleu by 1.0. (see ## conclusion this paper describes the techniques involved in our system submitted for the wmt20 similar language translation task by infosys. this winning hindi-marathi translation system is built based on nmt and evaluated based on the metric, bleu. the domain-based data preprocessing and filtering techniques eases model learning. adopting novel approach of anonymizing language agnostic tokens aided our system to focus more on tokens that matters in the translation. it is highly observed that the ratio of monolingual data used against bitext data plays a vital role in back-translated models. improving translation accuracy and language fluency by utilizing all available out-of-domain monolingual corpora to the maximum effect can be further explored.