JUNLP @ ICON 2020: Low Resourced Machine Translation for Indic Languages (2020)

Abstract: In the current work, we present the description of the systems submitted to a machine translation shared task organized by ICON 2020: 17th International Conference on Natural Language Processing. The systems were developed to show the capability of general domain machine translation when translating into Indic languages, English-Hindi, in our case. The paper shows the training process and quantifies the performance of two state-of-the-art translation systems, viz., Statistical Machine Translation and Neural Machine Translation. While Statistical Machine Translation systems work better in a low-resource setting, Neural Machine Translation systems are able to generate sentences that are fluent in nature. Since both these systems have contrasting advantages, a hybrid system, incorporating both, was also developed to leverage all the strong points. The submitted systems garnered BLEU scores of 8.701943312, 0.6361336198, and 11.78873307 respectively and the scores of the hybrid system helped us to the fourth spot in the competition leaderboard.

## introduction machine translation (mt) is the translation of one natural language to another using software. generally, training a good translation system requires the availability of a large and good quality parallel corpus. these corpora are easily available for languages that are spoken globally and have a large digital footprint. but finding the same for less-resourced languages, that are not universally recognized and do not have a large digital presence, is a challenge. this leads to the development of translation systems that do not produce quality results. the present work aims to solve a similar issue and focuses on showing the capability of general domain machine translation when translating into indic languages, english-hindi, in our case. the literature includes the description and training process of state-of-the-art translation systems and finally quantifies their performance with respect to the data provided as part of a shared task organized by icon 2020: 17th international conference on natural language processing foot_0 . we took part in the first sub-task and proceeded with developing translation systems with the help of the provided english-hindi parallel corpus. using the provided parallel corpus, we developed three systems. the first two systems was based on statistical machine translation (smt) and neural machine translation (nmt). for training the smt system, moses toolkit @xcite was used. the nmt system was a character based seq-to-seq model, that was trained using bi-directional long short-term memory (lstm) cells @xcite . the third system was a hybrid system, that works on the principles of automated post editing (ape). in this model, a transformer @xcite based nmt model was used to post edit the outputs, generated by an smt based translation system. the rest of the paper is organized as follows. section 2 describes the parallel corpus that was used to train the above-mentioned translation systems. section 3 contains the description and the training processes of all the developed translation systems. this will be followed by the evaluation results and discussion in section 4 and 5. finally, concluding remarks and future scopes have been discussed in section 6. ## parallel corpus multiple english-hindi parallel corpora were provided by the organizers for training the translation systems. among these, we decided on using the parallel corpus from cvit-pib foot_1 and cvit-mkb foot_2 . another high-quality corpus from tdil foot_3 was also used to train our developed systems. the number of parallel sentences in the cvit-mkb dataset was 5,272, in the cvit-pib dataset were 1,95,208, and in the tdil dataset were 50,000. in total, we were able to arrange for parallel english-hindi corpora of 2,50,480 sentences. the data was then tokenized to be used for our further experiments. for tokenizing the english data, nltk foot_4 @xcite was used and for tokenizing the hindi data, indic nlp library foot_5 (kunchukuttan, 2020) was used. ## machine translation after the english-hindi parallel corpora were compiled, we proceeded to develop our mt systems. as discussed earlier, the first two mt systems were based on smt and nmt. the third mt system was a hybrid system, using both smt and nmt, based on the transformer architecture, and worked on the principle of ape. the description of the all the three systems and the training process for the same is given in sections 3.1, 3.2 and 3.3 respectively. ## evaluation for evaluation purposes, the organizers provided us with a test data of 507 sentences. upon evaluation, the performance of our systems was calculated using bleu @xcite metric and they are shown in ## discussion from ## conclusion the present paper describes the systems submitted to the translation shared task organized by icon 2020: 17th international conference on natural language processing. we participated in the english-hindi translation task and the training data belonged to the general domain. three systems, smt, nmt, and a hybrid model was trained using these data. the models were pretty straightforward and did not contain any recent research advancements in the field of machine translation. as a future prospect, we would like to experiment with transfer learning methods, that learn from large data, and incorporate the knowledge onto models, trained using fewer data. this would be a good option as all the language options of the shared task were indic languages and good quality and robust multi-lingual translation system can be built out of it.