NUIG -Panlingua- KMI H indi- M arathi MT Systems for Similar Language Translation Task @ WMT 2020 (2020)

Abstract: NUIG-Panlingua-KMI submission to WMT 2020 seeks to push the state-of-the-art in Similar Language Translation Task for Hindi↔Marathi language pair. As part of these efforts, we conducteda series of experiments to address the challenges for translation between similar languages. Among the 4 MT systems prepared under this task, 1 PBSMT systems were prepared for Hindi↔Marathi each and 1 NMT systems were developed for Hindi↔Marathi using Byte PairEn-coding (BPE) into subwords. The results show that different architectures NMT could be an effective method for developing MT systems for closely related languages. Our Hindi-Marathi NMT system was ranked 8th among the 14 teams that participated and our Marathi-Hindi NMT system was ranked 8th among the 11 teams participated for the task.

## introduction developing automated relations between closely related languages is a contemporary concern especially in the domain of machine translation(mt). hindi and marathi exhibit a significant overlap in their vocabularies and strong syntactic plus lexical similarities. these striking similarities seem promising in enhancing the possibility of mutual inter-comprehension within closely related languages. however, automated translation between such closely related languages is a rather challenging task. the linguistic similarities and regularities in morphological variations and orthography motivate the use of character-level translation models, which have been applied to translation @xcite and transliteration @xcite @xcite . in the past few years, neural machine translation systems have achieved outstanding performance with high resource languages, with the help of open source toolkit such as opennmt @xcite , marian @xcite and neamtus @xcite , which provide various ways of experimenting with the use of different features and architectures, yet it fails to achieve the same results with low resource languages @xcite . however, @xcite revisited the nmt models and tuned hyperparameters, changed network architectures to optimize nmt for low-resource conditions and concluded that low-resource nmt is very sensitive to hyper-parameters such as byte pair encoding (bpe) vocabulary size, word dropout, and others. this paper is an extension of our work @xcite submitted to wmt 2019 similar language translation task. therefore our team adapted methods of the low resource setting for nmt proposed by @xcite to explore the following broad objectives: ## system description this section provides an overview of the systems developed for the wmt 2020 shared task. in these experiments, the nuig-panlingua-kmi team explored two different approaches: phrase-based statistical @xcite , and neural method for hindi-marathi and marathi-hindi language pairs. in all the submitted systems, we use the moses @xcite and nematus @xcite toolkit for developing statistical and neural machine translation systems respectively. the preprocessing was done to handle noise in data (for example, different language sentences, non-utf characters etc), the details of which are provided in section 3.1 ## experiments this section briefly describes the experiment settings for developing the systems. ## evaluation all the systems were evaluated using the reference set provided by the shared task organizers. the standard mt evaluation metrics, bleu @xcite score, ribes @xcite and ter @xcite , were used for automatic evaluation. these results were prepared on the primary and contrastive system submission which are mentioned in the ## conclusion our experiment results reveal that subword based nmt could take advantage of the relation between the similar language to boost the accuracy of neural machine translations system in low resource data settings. as bpe units are variable-length units and the vocabularies used are much smaller than morpheme and word-level model, the problem of data sparsity does not occur. on the contrary, it provides an appropriate context for translation between similar languages. however, the quality of data used to train the systems does affect the quality of translation. thus, we could conclude that shared features between two languages could be an advantage to leverage the accuracy of nmt systems for closely related languages.