Unsupervised Approach for Zero-Shot Experiments: B hojpuri– H indi and M agahi– H indi@ L o R es MT 2020 (2020)

Abstract: This paper reports a Machine Translation (MT) system submitted by the NLPRL team for the Bhojpuri–Hindi and Magahi–Hindi language pairs at LoResMT 2020 shared task. We used an unsupervised domain adaptation approach that gives promising results for zero or extremely low resource languages. Task organizers provide the development and the test sets for evaluation and the monolingual data for training. Our approach is a hybrid approach of domain adaptation and back-translation. Metrics used to evaluate the trained model are BLEU, RIBES, Precision, Recall and F-measure. Our approach gives relatively promising results, with a wide range, of 19.5, 13.71, 2.54, and 3.16 BLEU points for Bhojpuri to Hindi, Magahi to Hindi, Hindi to Bhojpuri and Hindi to Magahi language pairs, respectively.

## introduction for the past few years, neural machine translation (nmt) @xcite has been the favoured approach for both high resource and low resource languages translation. a large number of variations of the transformer @xcite model, such as bert @xcite , bart @xcite , mbart @xcite have been developed, giving state-of-the-art result for translation. however, successful nmt is only possible if a large parallel corpus is available to train the model. however, not so much parallel corpus is available for extremely low resource languages and there are still many languages in which no parallel corpora are available. creating data, even parallel corpus, for extremely low resource languages is a time and labour consuming process. instead of creating the data from scratch for such low resource languages, there is a possibility to use the available resources and tools of high resource languages for creating the resources and tools for low resource languages. in this paper, we use the available nepali-hindi low resource data for domain adaptation @xcite , followed by a back-translation @xcite approach for creating the synthetic dataset and nmt tools for bhojpuri-hindi and magahi-hindi zero-shot languages. we evaluate our method using a transformerbased nmt system @xcite for domain adaptation and backtranslation of monolingual data. our approach gives new state-of-the-art results of 19.5, 13.71, 2.54, and 3.16 bleu points for @xmath0, @xmath1, @xmath2, respectively. ## background ojha (2019) describe the resources created for bhojpuri languages and it also covers some results on bhojpuri-english smt systems. in 2019, loresmt @xcite organized the shared task for bhojpuri-english and magahi-english language pairs. however, previous works contains some parallel sentences. the work in this paper is primarily based on unsupervised domain adaptation @xcite , followed by backtranslation of monolingual data @xcite . unsupervised domain adaptation is the task of training a model on labelled data from a source domain to achieve better performance on data from a target domain, with access to only unlabeled data in the target domain. suppose d @xmath3, y} is domain of source task, where x is a source instance, and y is a labelled instance, and d @xmath4, where z is an unlabeled instance. then, the goal of unsupervised domain adaptation is to improve the accuracy of the target domain with the help of the source domain, where source and target domains are two different tasks that are related to each other in some relevant way. we apply domain adaptation to similar languages. hindi, nepali, bhojpuri and magahi are similar languages and they are also orthographically similar 1 (mundotiya et al., 2020). all these languages use the devanagari script and they share a lot of cognates and loan words among each other. this is the main motivation behind using unsupervised domain adaptation on similar languages in our paper. we use back-translation to increase the parallel corpus that helps in improving the bleu points @xcite for the translation task. ## methodology we used an unsupervised hybrid approach to train the model for zero parallel training data. ## experimental settings we used fairseq @xcite sequence modelling toolkit for training the transformer-based nmt model. the hyper-parameter settings used in the paper are described in ## conclusion our results show that by using domain adaptation for similar languages, an unsupervised approach gives varying degrees of improvement for translation of zero-shot languages. we note that similar language data and models can play a significant role in future for creating the resources and tools for extremely low or zero resource languages. we also used back-translation of monolingual data that also gives an improvement in scores. this work is evaluated on the dataset provided by loresmt 2020 shared task organizers. evaluation is performed using the metrics bleu, ribes, precision, recall and f-measure. results are reproducible using the publicly available fairseq toolkit.