IRLAB - DAIICT @ D ravidian L ang T ech- EACL 2021: Neural Machine Translation (2021)

Abstract: This paper describes our team’s submission of the EACL DravidianLangTech-2021’s shared task on Machine Translation of Dravidian languages. We submitted our translations for English to Malayalam , Tamil , Telugu and also Tamil-Telugu language pairs. The submissions mainly focus on having adequate amount of data backed up by good preprocessing of it to produce quality translations,which includes some custom made rules to remove unnecessary sentences. We conducted several experiments on these models by tweaking the architecture,Byte Pair Encoding (BPE) and other hyperparameters.

## introduction we participated in the shared task on machine translation in dravidian languages dravidian-langtech, eacl 2021 .the advancement of technology has increased our internet usage and majority of the languages have acclimatised to the growing digital world. however, there are many regional languages which are under-resourced languages and still lack development.one such language family is the dravidian languages , these languages are majorly spoken in south india ,nepal, pakistan, sri lanka and south asia, we have submitted our translations for three language pairs namely: our main focus was on proper pre-processing of the data and often we have seen that improper preprocessing has led to horrendous translations. we have done extensive data pre-processing starting basic cleaning of punctuation symbols to language specific script normalization , apart from this we have added some custom rules as well. which is followed by tokenization , truecasing and byte pair encoding (bpe). for indic languages espacially dravidian langauges we often face the problem of out of vocabulary word (oov) which is taken care by word segmentation using bpe ,so we deal with subwords instead of words. this paper is arranged as follows : first we describe the task undertaken which is followed by in-depth explanation of the model architecture, then next we have described the experimental setup which includes provided data set information , preprocessing steps and clean data statistics. after that , we describe the experiments conducted on different language pairs and analysis of the results produced. at last we draw some conclusions and propose some future work. ## task description the task focuses on improvement to access and production of information for speakers of dravidian languages. due to low resources available , the research community has not developed much of an interest in this domain , the main focus of this task is to promote research in this area and build machine translation systems for native monolingual speakers of these group of languages. in the era of digitization there is a large population who are not fully connected to the digital world because of their inability to access the digital world in their native language, which is what this task tries to accomplish. the experiment setup contains the detailed information about our experiments,data and vision. ## experimental setup we have tried to perform experiments on three different language pairs as mentioned in the introduction section. below mentioned is the detailed explanation of our approaches. ## results we made several models with different parameters and vocabulary sizes, ## conclusion and future works in this paper we describe our submission to machine translation for dravidian languages (eacl 2021). as the quality of the sentences was not good, we had do a lot of preprocessing steps. so we also added other open source parallel corpora for our training. our models are performing good on validation data but somewhat good on test data . for future works, we would like to try pivoting methods and transfer learning methods. we would also like to introduce semantic features such part of speech tags(pos), named entity tags(ner), lemmas etc. we can also use the language models for feature injection processes. apart from this, we would also like to employ semi-supervised and unsupervised methods into these language pairs. language pair bleu score english-malayalam 8.43 english-tamil 6.04 english-telugu 6.25