IIIT Hyderabad Submission To WAT 2021: Efficient Multilingual NMT systems for I ndian languages (2021)

Abstract: This paper describes the work and the systems submitted by the IIIT-Hyderbad team in the WAT 2021 MultiIndicMT shared task. The task covers 10 major languages of the Indian subcontinent. For the scope of this task, we have built multilingual systems for 20 translation directions namely English-Indic (one-to- many) and Indic-English (many-to-one). Individually, Indian languages are resource poor which hampers translation quality but by leveraging multilingualism and abundant monolingual corpora, the translation quality can be substantially boosted. But the multilingual systems are highly complex in terms of time as well as computational resources. Therefore, we are training our systems by efficiently se- lecting data that will actually contribute to most of the learning process. Furthermore, we are also exploiting the language related- ness found in between Indian languages. All the comparisons were made using BLEU score and we found that our final multilingual sys- tem significantly outperforms the baselines by an average of 11.3 and 19.6 BLEU points for English-Indic (en-xx) and Indic-English (xx- en) directions, respectively.

## introduction good translation systems are an important requirement due to substantial government, business and social communication among people speaking different languages. neural machine translation @xcite @xcite is the current state-of-the-art approach for machine translation in both academia and industry. the success of nmt heavily relies on substantial amounts of parallel sentences as training data @xcite which is again an arduous task for low resource languages like indian languages @xcite . many techniques have been devised to improve the translation quality of low resource languages like back translation @xcite , dual learning @xcite , transfer learning @xcite , etc. also, using the traditional approaches, one would still need to train a separate model for each translation direction. so, building multilingual neural machine translation models by means of sharing parameters with high-resource languages is a common practice to improve the performance of low-resource language pairs @xcite @xcite . low resource language pairs perform better when combined opposed to the case where the models are trained separately due to sharing of parameters. it also enables training a single model that supports translation from multiple source languages to a single target language or from a single source language to multiple target languages. this approach mainly works by combining all the parallel data in hand which makes the training process quite complex in terms of both time and computational resources @xcite . therefore, we are training our systems by efficiently selecting data that will actually contribute to most of the learning process. sometimes, this learning is hindered in case of language pairs that do not show any kind of relatedness among themselves. but on the other hand, indian languages exhibit a lot of lexical and structural similarities on account of sharing a common ancestry @xcite . therefore, in this work, we have exploited the lexical similarity of these related languages to build efficient multilingual nmt systems. covers 10 indic languages (bengali, gujarati, hindi, kannada, malayalam, marathi, oriya, punjabi, tamil and telugu) and english. the objective of this shared task is to build translation models for 20 translation directions (english-indic and indic-english). this paper is further organized as follows. section 2 describes the methodology behind our experiments. section 3 talks about the experimental details like dataset pre-processing and training details. results and analysis have been discussed in section 4, followed by conclusion in section 5. ## results and analysis we report the bleu score on the test set provided in the wat 2021 multiindic shared task. ## conclusion this paper presents the submissions by iiit hyderabd on the wat 2021 multiindicmt shared task. we performed experiments by combining different pre-processing and training techniques in series to achieve competitive results. the effectiveness of each technique is demonstrated. our final submission able to achieve the second rank in this task according to automatic evaluation.