Bidirectional Neural Machine Translation ( NMT ) using Monolingual Data for K hasi- E nglish Pair (2023)

Abstract: Due to a lack of parallel data, low-resource language machine translation has been unable to make the most of Neural Machine Translation. This paper investigates several approaches as to how low-resource Neural Machine Translation can be improved in a strictly low-resource setting, especially for bidirectional Khasi-English language pairs. The back-translation method is used to expand the parallel corpus using monolingual data. The work also experimented with subword tokenizers to improve the translation accuracy for new and rare words. Transformer, a cutting-edge NMT model, serves as the backbone of the bidirectional Khasi-English machine translation. The final Khasi-to-English and English-to-Khasi NMT models trained using both authentic and synthetic parallel corpora show an increase of 2.34 and 3.1 BLEU scores, respectively, when compared to the models trained using only authentic parallel dataset.

## introduction 1.introduction of machine translation machine translation is a sub-field of natural language processing that deals with the automatic translation of human languages. the translation can be text-text, speech-speech, speech-text and text-speech. text-based machine translation has come a long way, from rule-based translation, example-based translation, statistical machine translation (smt), and to neural machine translation (nmt). recurrent neural networks (rnn) have addressed the problems that rule-based and statistical machine translation approaches had in capturing exceptions in human languages and retaining word dependency. they are, however, slow to train and have limitations when it comes to modeling long-term dependencies. recent advancements in nmt have demonstrated outstanding efficiency by combining the encoder-decoder architecture with attention mechanism. nmt has become more popular in academia and industry as a result of advancements in the attention mechanism. using a self-attention mechanism, @xcite nmt model transformer has attained a state-ofthe-art bleu score in both english-to-german and english-to-french translations. by including nmt into the machine translation methodology, high-quality translation has been achieved. however, the performance of nmt is highly dependent on the size of the dataset. the performance of nmt on low-resource languages is marginal compared to the high-resource languages. therefore, it is necessary to find ways to make up for the shortage of resources in order to increase the translation quality. ## related works few works related to khasi and english translation have been reported. singh and hujon, 2020 has reported findings of the effectiveness of statistical and neural machine translation systems in domain-specific english to khasi translation. it was reported that the smt performed better than the nmt for this language pair. however, the performance of the smt model degraded as the sentence length increased. donald jefferson thabah and purkayastha, 2021 reported a cross-lingual language model pretraining system for bidirectional khasi-english machine translation. the model achieved a bleu score of 39.63 and 32.69 for translating english-khasi and khasi-english respectively when tested on similar domain test sentences. laskar et al., 2021 has reported the development of enkhcorp1.0, a corpus for english-khasi pair, and implemented baseline systems for english to khasi and khasi to english translation based on the neural machine translation approach. another recent work by hujon et al., 2023, discussed the experiments and improvement of the results of neural machine translation using transfer learning for the english-khasi language pair. the study reported that the joint vocabulary of three languages, english, french and khasi has contributed to the outstanding performance of nmt transfer learning model as compared to the nmt baseline model. ## methodology the methodologies employed in this current work can be broadly grouped into four parts, namely : parallel corpus creation, data preprocessing, sub-word tokenization, modelling of khasi-english mt and back-translation of khasi monolingual sentences. ## conclusion english-sen-2 meghalaya village council files fir against scribe patricia mukhim for social media post on assault case. khasi-ref-2 ka dorbar shnong ha meghalaya ka ai fir ia patricia mukhim na bynta ki jingthoh ha social media halor ki case ba leh donbor. t base ki shnong meghalaya ki rim ia ki jingdon jingem kiba kordor pyrshah ia ka jingpynpoi ia ki lad social media katkum ka juk mynta . t base +bpe ka jylla meghalaya ka peit bniah ia u high commissioner uba ki social media ki dang pyrshang ban kurup ia ka rynsan social media . t base +bpe+back ka jylla meghalaya ka peit bniah ia u high commissioner uba ki social media ki dang pyrshang ban kurup ia ka rynsan social media . expanding the machine translation model's effectiveness involves training it with an increased volume of parallel sentences. additionally, exploring augmentation techniques for generating parallel sentences could be beneficial, especially in scenarios with limited resources. notably, the sentence structures found in the bible differ significantly from contemporary sentence structures. to adeptly translate sentences from the current generation, the model must also be trained on up-to-date sentences, consequently expanding its vocabulary.