Neural Machine Translation for T amil– T elugu Pair (2021)

Abstract: The neural machine translation approach has gained popularity in machine translation because of its context analysing ability and its handling of long-term dependency issues. We have participated in the WMT21 shared task of similar language translation on a Tamil-Telugu pair with the team name: CNLP-NITS. In this task, we utilized monolingual data via pre-train word embeddings in transformer model based neural machine translation to tackle the limitation of parallel corpus. Our model has achieved a bilingual evaluation understudy (BLEU) score of 4.05, rank-based intuitive bilingual evaluation score (RIBES) score of 24.80 and translation edit rate (TER) score of 97.24 for both Tamil-to-Telugu and Telugu-to-Tamil translations respectively.

## introduction machine translation (mt) works as an interface that handles language ambiguity concerns via automatic translation between two different languages. neural machine translation (nmt) attains state-ofthe-art results for both high and low-resource language pairs translation @xcite @xcite @xcite @xcite . the nmt utilizes an artificial neural network to predicts the likelihood of a sequence of words. but nmt requires a sizeable parallel corpus to get effective mt output, challenging for low-resource pair translation. in this wmt21 shared task, we have participated on a similar language pair translation task of tamil-telugu pair using nmt. we aim to utilize similarity features among such a similar language pair and monolingual data to overcome the less availability of parallel corpus. the transformer model @xcite based nmt is considered in this work, since it outperforms rnn based nmt. moreover, nmt performance can be enhanced utilizing monolingual data @xcite @xcite @xcite . to evaluate the performance of our system's output, wmt21 organizer used standard evaluation metrics, namely, bleu @xcite , ribes @xcite and ter @xcite which are reported in section 4. ## related work there are limited works on the tamil-telugu pair @xcite . the literature survey found similar works on indian similar language pairs, such as hindi-nepali @xcite and hindi-marathi @xcite at wmt19 and wmt20. both @xcite used transformer model based nmt. moreover, @xcite ; @xcite ; @xcite pre-trained methods are incorporated in nmt to utilize advantage of monolingual data for low-resource pairs translation. in this work, glove @xcite pretrained word embeddings are used in transformer model @xcite based nmt for both tamil-to-telugu and telugu-to-tamil translation. ## system description our system mainly consists of the following parts: data preprossessing, model training and testing. these have been described in the following subsections. the dataset description is presented in section 3.1. for our system, we have used the opennmt-py toolkit @xcite for the data preprocessing, training and testing. ## result our system's outputs were submitted to the organizer for evaluation. consequently, the results of the shared task on "similar language translation" were announced separately for tamil-to-telugu foot_2 and telugu-to-tamil foot_3 . the ranking of the systems is mainly based on bleu score, while the ribes and ter scores are also given. our team name is cnlp-nits. for the tamil-to-telugu translation system, we achieved 4th rank with a bleu score of 4.05 and 6th rank with a bleu score of 4.05 for the telugu-to-tamil translation. the results of our system are reported in the ## conclusion and future work this work reports our system description along with results, which we have participated in the wmt19 shared task of similar language pair: tamil-telugu. both direction of translations, transformer model based nmt is used and utilized monolingual data through pre-trained word embeddings. we will investigate multilingual nmt approach in future to improve such low-resource translation quality.