Improving Neural Machine Translation for S anskrit- E nglish (2020)

Abstract: Sanskrit is one of the oldest languages of the Asian Subcontinent that fell out of common usage around 600 B.C. In this paper, we attempt to translate Sanskrit to English using Neural Machine Translation approaches based on Reinforcement Learning and Transfer learning that were never tried and tested on Sanskrit. Along with the paper, we also release monolingual Sanskrit and parallel aligned Sanskrit-English corpora for the research community. Our methodologies outperform the previous approaches applied to Sanskrit by various re- searchers and will further help the linguistic community to accelerate the costly and time consuming manual translation process.

## introduction sanskrit is one of the oldest, extensively studied, and researched languages in the world. 1 it is the oldest indo-aryan language prominently used in indo-european studies and now used for interlingual translation to english and many other indian languages, however, the fact that it is dead in today's time cannot be denied. english has emerged as the most popular language on the world level, and the advent of globalization has led to the need for cross-language translations. the developing regions still used the regional languages, and thus the translation of the english language into local languages can make information accessible. machine translation is one of the most onerous tasks in natural language processing. sanskrit is unique as it does not work using a noun-phrase model. 2 it's strict grammar rules, and syllables match being a direct parent of modern hindi language. the challenges faced during machine translation of sanskrit to other languages are translation divergence or the ambiguity phrase due to multiple-meaning, the lack of parallel language data. lots of historical and cultural data such as bhagavad gita, ramayana, mahabharata, and hindu literature vedas were originally written in the sanskrit language, and most of them are untranslated to other languages. despite its important part in indian culture and history, not much work has been done for translation to or from the sanskrit language. although the past few years, many efforts have been made to translate sanskrit to other languages using various machine translation approaches. @xcite and @xcite implemented example-based and rulebased approaches for sanskrit-english machine translation. later @xcite improved the rule-based machine translation approach by integrating with the artificial neural network (ann) model. recently @xcite proposed an encoder-decoder based neural machine transition approach for sanskrit to english. in recent years neural machine translation techniques like sequence to sequence learning, encoder-decoder attention-based architectures @xcite , and transformers have achieved state of the art (sota) results for supervised machine translation tasks. however, for low resource methods like back translation @xcite , cross-language modeling, phrase-based machine translation @xcite , and dual learning mechanism based upon reinforcement learning @xcite takes the benefit of monolingual data to improve the quality of translations over supervised approaches. unfortunately, none of the above methods has been used for sanskrit's machine translation task due to the lack of linguistic resources. ## dataset for this paper, we extracted parallelly allied english-sanskrit data as well as monolingual data for each language. the parallel english-sanskrit ## proposed methodology previous neural machine translation approaches for sanskrit mainly focus on rule-based approach and encoder-decoder mechanism using lstm units. the classical rule-based approach is timeconsuming, requires much manual work by the linguist, and does not have good learning capabilities. in contrast, lstms based models tend to overfit faster, suffer from issues related to polysemy, and multiple word senses @xcite . to handle all these issues, we first established a baseline translation model using a multi-head self-attention mechanism using encoder-decoder architecture, as suggested by @xcite . further, we improved the baseline translator using a reinforcement learning approach by establishing language models and agents that leverage monolingual data. we further experimented with the transfer learning approach for machine translation to get the benefit of lexically similar hindi language that is rich resource language. ## result & discussion the baseline model in section 4.1 was implemented using the opennmt framework @xcite . for the transfer learning implementation, we used the nematus toolkit @xcite . the baseline and child model in the transfer learning approach were trained, tuned, and tested on the same data split set discussed in section 3. for the quantitative evaluation, we used the bleu score @xcite for the qualitative analysis, five sanskrit language experts were randomly given 50 sentences each from all three models for the rating based on the following rating schema: all average ratings are shown in the last column of few observations from results: ## conclusion in this paper, we explored approaches that have never before been used for the translation of the sanskrit language to english. firstly we established a baseline with the transformer architecture. further, we improved the transformer model with dual learning methodology and gained small improvement on bleu score. the best bleu score we observed was with the transfer learning method. although we will not like to make an explicit comment that transformers architecture is the first time explored in our research, a few unofficial repositories have worked and published the results. in the future, we would try to add more parallel data to improve the trained models' quality. we believe that our research would open the doors for many researchers, linguists, and students to work and explore sanskrit. dataset, training subroutine, and trained model is available at: https://github.com/ravneetdt u/improving-neural-machine-translation-f or-sanskrit-english