IACS - LRILT : Machine Translation for Low-Resource I ndic Languages (2023)

Abstract: Even though, machine translation has seen huge improvements in the the last decade, translation quality for Indic languages is still underwhelming, which is attributed to the small amount of parallel data available. In this paper, we present our approach to mitigate the issue of the low amount of parallel training data availability for Indic languages, especially for the language pair English-Manipuri and Assamese-English. Our primary submission for the Manipuri-to-English translation task provided the best scoring system for this language direction. We describe about the systems we built in detail and our findings in the process.

## introduction the ability to overcome linguistic barriers has emerged as the most critical issue in a society that is becoming increasingly interconnected. these linguistic barriers can be eliminated enabling effective communication among various linguistic communities, machine translation (mt) systems are not only capable of translating common languages but also less widely spoken or even endangered languages, ensuring that even marginalized communities can participate in the global conversation. the use of machine translation for regional indian languages is both an intriguing and challenging application. india has an intricate mix of languages and dialects spoken all over its broad territory, making it a linguistically diverse nation @xcite . despite being culturally stimulating, this diversity poses substantial obstacles to effective communication. by automating the translation process and opening up content to speakers of different regional indian languages, machine translation presents a viable remedy. due to deep learning, neural networks, and natural language processing developments, machine translation technology has made significant strides in recent years @xcite . however, there are particular difficulties that must be overcome in order to adapt these technologies to the intricate linguistic features of indian languages @xcite . these difficulties include, among other things e.g., multi-word expressions @xcite , the complexity of morphology, syntactic changes, and the scarcity of parallel training data @xcite . the challenge of producing accurate and relevant translations is further complicated by the requirement to preserve cultural nuances and context-specific meanings @xcite . however, the translation problem for indian regional languages is compounded by: indian languages often exhibit rich morphology, leading to variations in word forms and sentence structures. so working on indic languages has the challenge of designing translation models and techniques that address these complexities and constraints while achieving high-quality translations between indian regional languages, contributing to effective crosslingual communication and content accessibility in india's diverse linguistic landscape. research involving indian languages is not very common due to the scarcity of parallel corpora. @xcite using statistical machine translation (smt) with a small corpus ( 2,500 sentences), the assamese-english bidirectional mt system for assamese to english and english to assamese obtained bleu scores of 9.72 and 5.02, respectively. das and baruah (2014) investigated and reported a bleu score of 11.32 for assamese to english using smt using 8,000 tourism domain parallel sentences. ## results and analysis ## conclusion and future work in this paper, we discussed the models and procedures our team used for the language pairs assamese-english and english-manipuri. according to our experiments, we claimed that using language models like indicbart and mbart-large-50 results in improvement for the low-resourced individual languages results. we hope that this will enable us to develop more precise and superior translation models for languages and domains with limited resources specially for indian languages where there is a presence of large language diversity. we also believe that, as seen with manipuri, a language with very few resources for processing we can use languages close and similar to it to aid in its processing and create a better way of processing those low-resource languages. in future, we will include our models in online post-editing platforms @xcite @xcite .