A3-108 Machine Translation System for L o R es MT Shared Task @ MT Summit 2021 Conference (2021)

Abstract: In this paper, we describe our submissions for LoResMT Shared Task @MT Summit 2021 Conference. We built statistical translation systems in each direction for English ⇐⇒ Marathi language pair. This paper outlines initial baseline experiments with various tokenization schemes to train models. Using optimal tokenization scheme we create synthetic data and further train augmented dataset to create more statistical models. Also, we reorder English to match Marathi syntax to further train another set of baseline and data augmented models using various tokenization schemes. We report configuration of the submitted systems and results produced by them.

## introduction machine translation systems are systems which translate from source language to target. there are multiple ways of creating such a system -rule based, data driven, hybrid etc. we are using data driven methods to create translation system. in data driven methods -statistical @xcite and neural methods @xcite have been employed to build decent mt systems in resource setting like english ⇐⇒ french. in loresmt shared task @xcite we are dealing with low resource setting for english, marathi pair. according to @xcite , compared to statistical methods neural methods have a drawback when used in low resource setting. hence, for this shared task we are using only phrase based statistical models to build translation models using moses foot_0 @xcite . marathi is morphologically richer, agglutinative language when compared to english. also, former follows sov as canonical syntactic structure while latter follows svo. level of difference in morphological richness and syntactic divergence between the two languages suggests to look for methods which can help to address them to certain extent in phrase based statistical models. since we are in low resource setting, to address data sparsity problem, we use various tokenization schemes, e.g. bpe @xcite , morfessor @xcite . combinations of these tokenization schemes are used with smt based method to create a baseline systems. after checking the optimal tokenization scheme, we use that scheme to augment training data with synthetic dataset using back translation @xcite . as was the case in baseline systems, augmented dataset goes through prepossessing with various tokenization schemes and smt method to build more systems. we elevate the amount of learning, the reordering model of smt has to do, by making use of rule based reordering system @xcite , @xcite to reorder english to match marathi syntax. with this we build another set of baseline systems for reordered english, marathi pair. like in baseline systems, mentioned above, here also we make use of various tokenization schemes. after comparing these schemes, we create synthetic dataset using back translation to augment reordered english, marathi pair. subsequent sections give more detailed overview of the systems developed. ## smt systems we use smt model to make initial baseline systems using various tokenization schemes. we further make use of rule based reordering model to create another set of baseline systems using reordered english, marathi pair. these two sets of systems are then used to create synthetic data set for data augmentation to train smt models. ## result for each language direction we submitted 72 output files.