NIT Rourkela Machine Translation( MT ) System Submission to WAT 2022 for M ulti I ndic MT : An Indic Language Multilingual Shared Task (2022)

Abstract: Multilingual Neural Machine Translation (MNMT) exhibits incredible performance with the development of a single translation model for many languages. Previous studies on multilingual translation reveal that multilingual training is effective for languages with limited corpus. This paper presents our submission (Team Id: NITR) in the WAT 2022 for “MultiIndicMT shared task” where the objective of the task is the translation between 5 Indic languages from OPUS Corpus (which are newly added in WAT 2022 corpus) into English and vice versa using the corpus provided by the organizer of WAT. Our system is based on a transformer-based NMT using fairseq modelling toolkit with ensemble techniques. Heuristic pre-processing approaches are carried out before keeping the model under training. Our multilingual NMT systems are trained with shared encoder and decoder parameters followed by assigning language embeddings to each token in both encoder and decoder. Our final multilingual system was examined by using BLEU and RIBES metrics scores. In future, we look forward to extend our research that will help in fine-tuning of both encoder and decoder during the monolingual unsupervised training in order to improve the quality of the synthetic data generated during the process.

## introduction this paper illustrates the submission of the multi-indicmt shared task at the 9th workshop on asian translation (wat 2022) @xcite ) by nit rourkela (team id: nitr). building machine translation (mt) model for 5 indic languages ( assamese(as), sindhi(sd), sinhala(si), urdu(ur) and nepali (ne)) to english and vice versa is the main goal of this shared task wherein nitr has taken part. these languages are newly added in wat 2022 corpus. the method that is most often used in machine translation is neural machine translation @xcite , @xcite . language pairs with fewer parallel corpora are often subject to have poor nmt performance. this happens because of a lack of translation expertise as well as overfitting, which is unavoidable in a low-resource environment. since many indian languages suffer from limited resources on an individual basis, creating high-quality machine translation systems for indian languages continues to be a difficult task. however, numerous methods, including back translation @xcite , transfer learning @xcite , etc., are developed to enhance the quality of low resource language translations. additionally, training is needed for the model in each translation direction using conventional methods. so, in order to enhance the performance of language pairs with low resources, it is standard procedure to develop multilingual neural machine translation(mnmt) models by sharing parameters with languages having high resources @xcite , @xcite , @xcite . hence, in this regard, the shared task for wat 2022 multiindicmt's goal is to verify the usefulness of mt methods for indian languages. we have provided two mnmt models: a) one for indic to english and the other for b) english to indic. nitr mt system is trained on two mnmt models (many to one and one to many) based on transformer architecture using wat 2022 mulitindic corpus. our mnmt systems are based on @xcite 's method, wherein a languagespecific token is appended to the input phrase in both one-to-many and many-to-many models to identify the target language to which the model needs to convert. our training corpus are cleaned up thoroughly by using a set of heuristics techniques because the transformer model is sensitive to training noise @xcite . finally, the result are presented in terms of bilingual evaluation understudy (bleu) @xcite and rank-based intuitive bilingual evaluation score (ribes) @xcite . in this paper, section 2 describes the related work which is followed by the the detail description of data set in section 3. the experimental methodology being explained in section 4. the findings with results are discussed in section 5, and the paper concludes in section 6. ## related work nmt framework can naturally include numerous languages, despite the fact that the early study on nmt focused on developing translation systems between two languages. as a result, research work on mt systems, that involves more than two languages, keeps on increasing significantly. recently, a lot of attention is paid to multilingual neural machine translation since it allows one single model to translate between different languages. a many-to-many paradigm for multi-way translation employing shared attention and languagespecific encoders and decoders is presented by @xcite . while transfer learning occurs implicitly in multilingualism, more explicit use of fine-tuning is an approach to accomplish the same @xcite . transliteration across scripts of related languages, as discussed in @xcite , may enhance the quality of multilingual models. likewise, different methods that can be utilized to implement mnmt systems are summarised by @xcite . @xcite ) employs a fixed crosslingual embedding, a single shared encoder, and language-specific decoders. by permitting positive transfer from the high resource languages, multiple studies on multilingual nmt emphasizes the benefits for language pairings with low resources, enhancing the quality of the low resource ones. in terms of the bleu score, multilingual unsupervised model tends to fare better than the bilingual unsupervised baselines. building on earlier research by @xcite , @xcite efforts are made to combine multilingual supervised mt, zero-resource mt @xcite , and self-supervised learning into a single model for 1000 languages. in the next section, we give detail about the dataset which we have used. ## dataset we used the dataset given by the organisers for generating the parallel corpus for assamese, nepali, sindhi, sinhala and urdu language. the organizer have shared the multiindicmt wat 2022 corpora, which is made up of roughly 15 million parallel sentences for 15 language pairs. from that corpus, we have used the opus corpus @xcite for the language pairs of assamese, nepali, sindhi, urdu, sinhala and english. no additional data is used from any other sources. ## methodology in this section, we give details about the system those are submitted to the wat2022 for multi-indicmt shared task @xcite . we present findings for two categories of models: a) many-en: multilingual many-to-one system trained with all parallel data of five language pairs that are provided in wat 2022, and b) en-many: multilingual many-to-one system trained with parallel data using the same corpus but in opposite direction. in this method, a shared encoder-decoder transformer architecture is employed to train our multilingual models. ## experimental setup this section describes the complete pipeline used to produce the translation systems for the wat multiindic shared task submission. ## results the quality of our translation files are evaluated by the organisers using bleu and ribes, based on metrics on the official wat 2022 multiindicmt test set @xcite . to determine the bleu scores of baseline models, multi-bleu.perl script is availed. when evaluating the multi-indicmt task, organizers prefer to tokenized reference and hypothesis files to find out the bleu score. moses-tokenizer is used for tokenization. we present results provided by the organizers for english to indic and indic to english language pairs which are based on the translation files that we have submitted @xcite . ## conclusion in this paper, we highlight the multiindicmt shared task as submitted by us to wat 2022. through provisioning of two multilingual nmt models, one-to-many (english to 5 indic languages) and many-to-one (4 indic languages to english) we get competitive outcomes. in our process, test-runs are executed combining with several preprocessing and training strategies sequentially. although we have used sufficient data filtering techniques, still it is observed that the training data gets contaminated with noise. therefore, investigating more efficient data filtering methods as well as their effects on mt performance is another promising future area. in future, we look forward to extend our research that will help in fine-tuning of both encoder and decoder during the monolingual unsupervised training in order to improve the quality of the synthetic data generated during the process.