NLPRL O dia- E nglish: Indic Language Neural Machine Translation System (2020)

Abstract: In this manuscript, we (team name is NLPRL) describe systems description that was submitted to the translation shared tasks at WAT 2020. We describe our model as transformer based NMT by using byte-level based BPE (BBPE). We used the OdiEnCorp 2.0 parallel corpus provided by the shared task organizer where the training, validation, and test data contain 69370, 13544, and 14344 lines of parallel sentences, respectively. The evaluation results show the BLEU score of English-to-Oria below the Organizer (1.34) and Oria-to-English direction shows above the Organizer (11.33).

## introduction in this paper, we (team name is nlprl) describe the system that we develop as part of our participation in the workshop on asian translation (wat) 2020 @xcite for the odia-english language pairs. for the first time, odia-english language pair is adopted for a translation task in the wat 2020. the shared task organizers provide odia-english (odiencorp 2.0) @xcite parallel corpus that contains train, valid, and test data collected by researchers at ufal (institute of formal and applied linguistics). the odia (old name is oriya) language is one of the official languages of odisha, situated in the eastern part of india. it belongs to the eastern indo-aryan group, which is a branch of indu-european languages. it comes under one of the 22 official languages recognized by the government of india. in this experiment, we use byte-level subwords, specifically byte-level bpe (bbpe) @xcite tokenizer, which is more efficient than pure bytes, worked on the transformer architecture. the primary motivation was to try out this model on odia-english parallel data provided by the organizers. we presented the translation results by using an automatic evaluation server prepared by shared task technical collaborators. the article is structured as follows. in section 2, we discuss our system description that covering the statistics of the dataset and its preparation with the experimental setup. section 3 describes results and analysis. finally, we conclude in section 4 with the conclusion and future work. there are many challenges for neural machine translation (nmt) than other approaches mentioned in different literature @xcite . most state-of-the-art nmt systems achieve outstanding results based on large parallel corpora only. it has been outperforming the traditional methods for machine translation, such as rule-based and statistical-based approaches. however, for the low resource languages, various techniques have been proposed for nmt, such as metalearning @xcite , transfer learning @xcite , which produced promising results. the two main strategies, namely pivot-based and zero-shot machine translation, were used to build nmt models without direct parallel data. the performance of nmt on the low resource languages mainly suffers due to the out-of-vocabulary (oov), the words not in the trained nmt model's vocabulary.. nmt models typically operate with a fixed vocabulary; however, the translation is an open-vocabulary problem. several approaches have been proposed to reduce the issue of oov. byte-pair-encoding (bpe) @xcite enables nmt model translation on openvocabulary by encoding rare and unknown words as a sequence of subword units. @xcite proposed bbpe, which builds a byte-level subword vocabulary for machine translation. ## system description this section covers a dataset, preprocessing, and the experimental setup required for our systems. ## result and analysis the organizers have evaluated the submitted predictions on the test set (of odiencorp 2.0 parallel corpus) of both directions pair in terms of bilingual evaluation understudy (bleu) @xcite , rank-based intuitive bilingual evaluation score (ribes) @xcite and adequacy-fluency metrics (am-fm) @xcite . ## conclusion and future work in this paper, we report our submitted system scores based on the official scores released by the wat 2020 shared translation task. we train our system for odia-to-english and english-to-odia language pairs using the transformer-based neural machine translation using a byte-level version of bpe. wat 2020 depicts three types of scores, namely bleu, ribes, and am-fm. the bleu score, a standard metric for machine translation evaluation of english to odia and odia to english on the benchmark test data, is 1.34 and 11.33, respectively. we found that our model performs better for @xmath2, compared to the organizer's model. as the next step, we would like to improve the evaluation score using a multi-lingual nmt model. furthermore, using monolingual source and target language can improve the translation score of both directions of the language pair.