Aakash Banerjee


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Multilingual Machine Translation Systems at WAT 2021: One-to-Many and Many-to-One Transformer based NMT
Shivam Mhaskar | Aditya Jain | Aakash Banerjee | Pushpak Bhattacharyya
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

In this paper, we present the details of the systems that we have submitted for the WAT 2021 MultiIndicMT: An Indic Language Multilingual Task. We have submitted two separate multilingual NMT models: one for English to 10 Indic languages and another for 10 Indic languages to English. We discuss the implementation details of two separate multilingual NMT approaches, namely one-to-many and many-to-one, that makes use of a shared decoder and a shared encoder, respectively. From our experiments, we observe that the multilingual NMT systems outperforms the bilingual baseline MT systems for each of the language pairs under consideration.

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Neural Machine Translation in Low-Resource Setting: a Case Study in English-Marathi Pair
Aakash Banerjee | Aditya Jain | Shivam Mhaskar | Sourabh Dattatray Deoghare | Aman Sehgal | Pushpak Bhattacharya
Proceedings of Machine Translation Summit XVIII: Research Track

In this paper and we explore different techniques of overcoming the challenges of low-resource in Neural Machine Translation (NMT) and specifically focusing on the case of English-Marathi NMT. NMT systems require a large amount of parallel corpora to obtain good quality translations. We try to mitigate the low-resource problem by augmenting parallel corpora or by using transfer learning. Techniques such as Phrase Table Injection (PTI) and back-translation and mixing of language corpora are used for enhancing the parallel data; whereas pivoting and multilingual embeddings are used to leverage transfer learning. For pivoting and Hindi comes in as assisting language for English-Marathi translation. Compared to baseline transformer model and a significant improvement trend in BLEU score is observed across various techniques. We have done extensive manual and automatic and qualitative evaluation of our systems. Since the trend in Machine Translation (MT) today is post-editing and measuring of Human Effort Reduction (HER) and we have given our preliminary observations on Translation Edit Rate (TER) vs. BLEU score study and where TER is regarded as a measure of HER.