IITP - MT at WAT 2021: Indic- E nglish Multilingual Neural Machine Translation using R omanized Vocabulary (2021)

Abstract: This paper describes the systems submitted to WAT 2021 MultiIndicMT shared task by IITP-MT team. We submit two multilingual Neural Machine Translation (NMT) systems (Indic-to-English and English-to-Indic). We romanize all Indic data and create subword vocabulary which is shared between all Indic languages. We use back-translation approach to generate synthetic data which is appended to parallel corpus and used to train our models. The models are evaluated using BLEU, RIBES and AMFM scores with Indic-to-English model achieving 40.08 BLEU for Hindi-English pair and English-to-Indic model achieving 34.48 BLEU for English-Hindi pair. However, we observe that the shared romanized subword vocabulary is not helping English-to-Indic model at the time of generation, leading it to produce poor quality translations for Tamil, Telugu and Malayalam to English pairs with BLEU score of 8.51, 6.25 and 3.79 respectively.

## introduction in this paper, we describe our submission to the multiindicmt shared task at the 8th workshop on asian translation 1 (wat 2021) @xcite . the objective of this shared task is to build machine translation (mt) models between 10 indic languages (bengali, gujarati, hindi, kannada, malayalam, marathi, odia, punjabi, tamil, telugu) and english. we submit two multilingual neural machine translation models (mnmt): one for @xmath0. multilingual machine translation @xcite @xcite @xcite ) has gained * equal contribution 1 our team id: iitp-mt popularity in recent times due to the ability to train a single model which is capable of translating between multiple language pairs. the main benefit of multilingual model is transfer learning. when a low resource language pair is trained together with a high resource pair, the translation quality of a low resource pair may improve @xcite . this method of training is more suitable for indic languages as they are similar to each other @xcite and relatively under-resourced when compared with european languages @xcite . romanization is the process of converting characters that are written in various scripts into latin script. @xcite showed that in a transfer learning setting, romanization improves the transfer between related languages that use different scripts. we train two mnmt models, which translate between indic languages and english with all indic data romanized. the models are evaluated using the bleu @xcite , ribes @xcite and amfm @xcite metrics. the paper is organized as follows. in section 2, we briefly mention some notable works on multilingual nmt and romanized nmt. in section 3, we describe the systems submitted along with preprocessing and romanization of indic data. results are described in section 4. finally, the work is concluded in section 5. ## related works multilingual machine translation enabled the ability to deploy a single model for multiple language pairs without training multiple models. @xcite proposes a multi-task learning framework to translate one source language into multiple target languages by adding language specific decoders. their method has shown improvements over base-line models which are trained for individual language pairs. @xcite proposes a many-tomany model for multi-way, multilingual translation using shared attention and language specific encoders and decoders. however, with this setting, model parameters will increase as the number of languages increases. @xcite use shared encoder-decoder model in which multiple languages share both encoder and decoder also the attention module. this is achieved by combining multiple language pairs data into a single corpus and adding a language tag to every source sentence to specify its target language. this method enables the zero-shot translation, in which the model can generate sentences belonging to a language pair that is not seen at training time. @xcite show that multilingual nmt models are capable of handling large number of language pairs. freitag and firat (2020) proposes that the use of multi-way alignment information will improve the translation quality of language pairs for which training data is scarce in multilingual settings. improving the quality of nmt models with monolingual data is a common approach nowadays, especially in low resource settings. backtranslation @xcite is an effective approach to make use of target monolingual data. in this approach, with the help of existing target-tosource mt system target is translated into source and resulting synthetic parallel corpus is combined with clean corpus and used to train source-totarget nmt system. multi-task learning framework @xcite is another way to utilize monolingual data to improve the performance of nmt. recent studies @xcite @xcite show that the romanization will improve the performance of nmt system. however these approaches apply romanization at source side only. @xcite showed that romanization can be applied on the target side also followed by an additional, learned deromanization step. in this work, we follow @xcite method to train multilingual nmt models. we romanize indic data and use it to train our models. we also follow back-translation approach @xcite to create synthetic parallel data. we report the results of the models which are trained on combined synthetic and clean parallel corpus. ## system description this section describes datasets, preprocessing and experimental setup of our models. ## results and analysis the official bleu @xcite , ribes @xcite and amfm @xcite scores of the multilingual models are shown in ## conclusion in this paper, we describe our submission to the multiindicmt shared task to wat 2021. we submit two multilingual nmt models: many-to-one (10 indic languages to english) and one-to-many (english to 10 indic languages). we romanize all indic language data to convert all languages' tokens in roman script. we also generate synthetic data using the back-translation approach. we train our models on the romanized data sets which is a combination of clean corpora and synthetic backtranslated corpora. we evaluate our models using bleu, ribes and amfm scores and observed that many-to-one model achieves highest bleu score of 40.08 for hindi-english pair and one-tomany model achieves highest bleu score of 34.48 for english-hindi pair. however, the shared subword vocabulary at target side for the one-to-many model lead to the poor performance of the oneto-many model especially in tamil, telugu and malayalam to english pairs by achieving bleu score of 8.51, 6.25 and 3.79 respectively.