Evaluating the Performance of Back-translation for Low Resource E nglish- M arathi Language Pair: CFILT - IITB ombay @ L o R es MT 2021 (2021)

Abstract: In this paper, we discuss the details of the various Machine Translation (MT) systems that we have submitted for the English-Marathi LoResMT task. As a part of this task, we have submitted three different Neural Machine Translation (NMT) systems; a Baseline English-Marathi system, a Baseline Marathi-English system, and an English-Marathi system that is based on the back-translation technique. We explore the performance of these NMT systems between English and Marathi languages, which forms a low resource language pair due to unavailability of sufficient parallel data. We also explore the performance of the back-translation technique when the back-translated data is obtained from NMT systems that are trained on a very less amount of data. From our experiments, we observe that the back-translation technique can help improve the MT quality over the baseline for the English-Marathi language pair.

## introduction in this work, we explore various ways to perform machine translation (mt) in low resource settings, that is, when very less amount of parallel data is available to train the model. we also explore the performance of the back-translation technique, which is one of the data augmentation techniques to overcome the problem of low resource in neural machine translation. in our work, we focus on the neural machine translation (nmt) systems, which requires a large amount of parallel training data to produce good quality translations. this is the major reason behind nmt systems to be considered as data hungry. the language pair for which less amount of parallel data is available is considered a low resource language pair. as compared to parallel data, monolingual data is easier to obtain and is available in relatively large quantity. this monolingual data can also be used to improve the performance of the nmt system. we explore the back-translation technique to make use of the available monolingual data to create an augmented pseudo-parallel data which can then be used to train the nmt system. in back-translation, the monolingual data is first translated using a machine translation system. in case of low resource languages, this machine translation system is trained on very less amount of data and hence, the translation of the monolingual data produced may not be of very high quality. if the model is trained using this low quality back-translated parallel data, it can degrade the performance of the system. we explore the performance of the nmt system when it is trained using back-translation technique in which the back-translated data is generated from a nmt system which is trained on a less amount of data. from the experiments that we have performed as part of the loresmt 2021 @xcite ) task, we observe that the back-translation technique gives a bleu score improvement of up to 1.2 points over the baseline model for the english-marathi mt task. ## related work neural machine translation systems were initially based on recurrent neural network (rnn) based approaches @xcite . but recurrent neural network based architectures were not able to capture long term dependencies in long sentences. in order to overcome this problem, attention @xcite mechanism was introduced. the attention-based rnn architecture still suffered from problems like longer training time because of their sequential nature. later transformer architecture @xcite was introduced which improved the performance of the nmt systems and also lead to faster training due to its non-sequential nature. @xcite introduced the technique of back-translation in which monolingual data is used to create augmented pseudo-parallel data. @xcite used statistical machine translation (smt) system by extracting phrases generated during smt training and using them along with the training data for nmt systems. @xcite introduced a transfer learning techniques in which a parent nmt model is initially trained on a high resource language pair and then the parameters of this parent model are used to initialize a child model, which is then trained on a low resource language pair. @xcite introduced a transfer learning technique based on a pivot language in which a pivot language is used to pre-train the encoder and decoder of a nmt model, which are then used to initialize the encoder and decoder of the final nmt model which is then fine-tuned on the low resource language pair. multi-lingual nmt models @xcite @xcite that can translate to or from multiple languages have shown performance improvements in the case of low resource language pairs when the system also includes high resource language pairs. ## approaches in this section, we discuss the various techniques that we have used to implement our english-marathi and marathi-english mt systems. ## experiments in this section, we discuss the various experiments that we have performed as a part of this work. we used the english-marathi parallel corpus provided by the loresmt 2021 organizers, which consisted of 20,933 english-marathi parallel sentences. further, for our back-translation experiment we used the marathi monolingual corpus provided by the loresmt 2021 organizers which consisted of 21,902 marathi sentences. ## results and analysis ## conclusion in this work, we implement various english-marathi and marathi-english baseline nmt systems and use the given monolingual marathi data to implement the back-translation technique for data augmentation. from our experiments, we observe that the technique of back-translation can help improve the mt quality over the baseline for the english-marathi mt task for which less amount of parallel data is available.