E n K h C orp1.0: An E nglish– K hasi Corpus (2021)

Abstract: In machine translation, corpus preparation is one of the crucial tasks, particularly for lowresource pairs. In multilingual countries like India, machine translation plays a vital role in communication among people with various linguistic backgrounds. There are available online automatic translation systems by Google and Microsoft which include various languages which lack support for the Khasi language, which can hence be considered lowresource. This paper overviews the development of EnKhCorp1.0, a corpus for English–Khasi pair, and implemented baseline systems for EnglishtoKhasi and KhasitoEnglish translation based on the neural machine translation approach.

## introduction the khasi language (also spelled khasia, khas see, cossyah, or kyi) is primarily spoken by peo ple living in the region surrounding the khasi and jaintia hills of meghalaya state in india. it is a member of the monkhmer linguistic branch of the austroasiatic language family. khasi is an associate official language foot_0 in meghalaya since 2005. according to the 2011 census of india, there are around one million native speakers of khasi foot_1 . khasi has significant dialectal variation, some of them being sohra khasi, mawlai khasi, pnar, nongkrem khasi, mylliem khasi, bhoi khasi nonglung, war and maram. khasi has a subject verbobject (svo) sentence structure, similar to english but unlike most of the indian languages @xcite . khasi contains several words bor rowed from indoaryan languages, mainly from bengali and hindi. in the past, the khasi language had no script of its own. the welsh missionary thomas jones foot_2 , in 1841, wrote the language in the latin script. as a result, the latin alphabet of the language has a few similarities with the welsh al phabet. khasi in latin script has a 23letter alpha bet. ## enkhcorp1.0 this section overviews the corpus. section 2.1 de scribes the contents of the corpus. section 2.2 and 2.3 present the data extraction technique and do main coverage. ## baseline system in the area of mt, the nmt achieves a stateof theart approach for both high and lowresource language pairs bahdanau et al. ( 2015 ## conclusion and future work this paper presents enkhcorp1.0, where we have developed a parallel corpus of english-khasi par allel and khasi monolingual data. it can be used in various nlp tasks, including mt. the dataset will be publicly available here: https://github. com/cnlp-nits/enkhcorp1.0 along with necessary licence agreement. by utilizing this corpus, we have built nmt baseline systems for translation to and from khasi. we will increase the corpus size in the future and perform more experiments with ad vanced deep learning techniques to improve trans lation accuracy.