Investigation of Multilingual Neural Machine Translation for I ndian Languages (2022)

Abstract: In the domain of natural language processing, machine translation is a well-defined task where one natural language is automatically translated to another natural language. The deep learning-based approach of machine translation, known as neural machine translation attains remarkable translational performance. However, it requires a sufficient amount of training data which is a critical issue for low-resource pair translation. To handle the data scarcity problem, the multilingual concept has been investigated in neural machine translation in different settings like many-to-one and one-to-many translation. WAT2022 (Workshop on Asian Translation 2022) organizes (hosted by the COLING 2022) Indic tasks: English-to-Indic and Indic-to-English translation tasks where we have participated as a team named CNLP-NITS-PP. Herein, we have investigated a transliteration-based approach, where Indic languages are transliterated into English script and shared sub-word level vocabulary during the training phase. We have attained BLEU scores of 2.0 (English-to-Bengali), 1.10 (English-to-Assamese), 4.50 (Bengali-to-English), and 3.50 (Assamese-to-English) translation, respectively.

## introduction due to the advancement of deep learning techniques, neural machine translation (nmt) attains remarkable progress for single pairs translation with a large amount of bilingual corpus @xcite @xcite . moreover, nmt shows good translational performance for low-resource indian languages @xcite @xcite @xcite . recent years, researchers have been investigating multilingual nmt from various aspects, zero-shot, pivot-based, and different settings, namely, manyto-one, one-to-many, many-to-many @xcite . in @xcite , authors developed samanantar, a parallel dataset for 11 indian languages. they converted all indic data into a common devanagari script and took the advantage of lexical sharing at the sub-word level for transfer learning during the training process. they explored multilingual nmt models for english-to-indic and vice-versa by considering language tags for indicating indic languages on the source side @xcite . similarly, we have investigated multilingual nmt in the indic tasks of wat2022. the difference is that instead of converting all indic data into a common devanagari script, we have converted all indic data into english script and attempted to take the benefits of lexical sharing at the sub-word level for both source and target languages. the rest of the paper is organized as follows: section 2 presents the review of related works. the system description is briefly discussed in section 3. section 4 reports the results and section 5 concludes the paper with future scope. ## related work the literature survey finds out very limited work on multilingual nmt, specifically, for englishto-indic and indic-to-english translation @xcite . they contributed samanantar dataset which comprises parallel corpora of 11 indic languages with english side parallel sentences and explored the multilingual nmt model for english-to-indic and indic-to-english. they used fairseq @xcite toolkit for transformer-based model training via multilingual settings of many-to-one and one-to-many @xcite . ## system description we have employed the opennmt-py @xcite toolkit to build multilingual transformer-based nmt models for englishto-indic and indic-to-english translation. we have used parallel corpora provided by the wat2022 organizers @xcite . additionally, we have used english-assamese parallel corpus @xcite . we have maintained the equal ratio (1 : 1) for eng-indic (asm/ben/guj/hin/kan/mal/mar/tel/tam/pan/npi/ ory) language pairs of the dataset in the multilingual nmt settings and data statistics are presented in ## results the wat2022 shared task organizer @xcite published the evaluation result foot_1 (indic22en-as/indic22as-en/indic22enbn/indic22bn-en) at the indic translation task for english-to-indic and indic-to-english and our team achieve the second position for english-to-assamese and vice-versa translation. we have participated with a team name cnlp-nits-pp in the english-assamese and english-bengali submission tracks of the same task where a total of two teams participated. the automatic evaluation metrics, bleu @xcite , ribes @xcite are used for evaluation of results. ## conclusion and future work in this work, we have investigated multilingual nmt for indic task of wat2022 by taking the advantage of sub-word level source-target lexical sharing during the training. however, we need to do more experiments to improve the translational performance of low-resource pairs by utilizing pretrained multilingual models. .