Impacts of Approaches for Agglutinative- LRL Neural Machine Translation ( NMT ): A Case Study on M anipuri- E nglish Pair (2023)

Abstract: Neural Machine Translation (NMT) is known to be extremely challenging for Low-Resource Languages (LRL) with complex morphology. This work deals with the NMT of a specific LRL called Manipuri/Meeteilon, which is a highly agglutinative language where words have extensive suffixation with limited prefixation. The work studies and discusses the impacts of approaches to mitigate the issues of NMT involving agglutinative LRL in a strictly low-resource setting. The research work experimented with several methods and techniques including subword tokenization, tuning of the selfattention-based NMT model, utilization of monolingual corpus by iterative backtranslation, embedding-based sentence filtering for back translation. This research work in the strictly low resource setting of only 21204 training sentences showed remarkable results with a BLEU score of 28.17 for Manipuri to English translation.

## introduction machine translation (mt) is the process of translating text automatically, without human intervention, from one language to another using machines in general and computers in particular. neural machine translation (nmt) systems, which are based on neural networks, are able to perform on par with human translators @xcite . but these systems were trained with data sets that have tens or even hundreds of millions of parallel sentences. large-scale data sets are only accessible for a select few high-resource language pairs. a language that is considered low-resource or under-resourced is one that lacks electronic tools, resources, linguistic expertise or a distinctive or reliable writing system (orthography) @xcite . in the context of mt, low-resource languages (lrls) are those that do not have enough parallel data sets to train the deep learning models. manipuri/meeteilon is one such low-resource language. it is the state language of the north eastern indian state of manipur and is one of the official languages specified in the eighth schedule of the constitution of india. it is classified as a tibeto-burman language and is known to be highly agglutinative @xcite . the foundational natural language processing (nlp) works on manipuri language has been slowly but steadily picking up pace in the last decade or so @xcite ; ?; @xcite @xcite @xcite @xcite @xcite @xcite . although few attempts have been made to collect parallel corpus and to build efficient translation models, the low-resource nature and the complex morphology of the language has hampered the progress to attain tangible successes. therefore, innovative ways has to be studied to improve the translation accuracy in the lowresource settings. this work is a serious attempt to study the impacts of experimention with innovative methods to improve the translation accuracy of manipuri (mni) to english (en) nmt in a strictly low-resource setting. the paper is organized as follows. section 2 discusses the literature review of previous works conducted in the area, section 3 discusses the methods of the experiments conducted and section 4 dis-cusses the results of the experiments. finally section 5 outlines the conclusion and aspects for future work. ## literature review machine learning (ml) has been boosted by the rediscovery of neural networks @xcite . the introduction of nmt has enabled the use of a single large neural net that directly transforms the source sentence into the target sentence @xcite @xcite . different neural architectures have been proposed with the goal of improving efficiency of translation. this includes recurrent networks @xcite @xcite , convolutional networks @xcite @xcite and transformer networks @xcite . few progress have recently been made regarding the research in manipuri nmt also. singh and bandyopadhyay (2010c) reported an example-based mt system for manipuri and english. statistical models were also explored (singh and bandyopadhyay, 2010b,a; @xcite . attempts to integrate reduplicated multiword expressions (rmwes) into the phrase based statistical machine translation (pbsmt) to improve translation quality has also been reported @xcite . studies on the name entity recognition (ner), morpheme identification etc. of manipuri to assist downstream nlp pipelines have been worked upon @xcite . investigations were also reported with different supervised and unsupervised methods of statistical machine translation (smt) and nmt @xcite @xcite . @xcite studied the normalization of the morphological inflection issue of manipuri, and to induce inter-language connecting points between manipuri and english. singh and singh (2022a) used long short-term memory (lstm) based many-to-many multilingual nmt system that is infused with cross-lingual features. @xcite worked on devising an efficient manipuri-english au-tomatic sentence aligner based on embeddings. @xcite studied on a pretrained word embedding for manipuri. @xcite reported the study on multimodal machine translation (mmt). @xcite attempted with a factored model of smt with a part-of-speech (pos) tag as a factor to incorporate linguistic information about the languages followed by hand-coded reordering. improving the mt systems by using the monolingual data to solve the parallel sentences dependency issue has been reported @xcite . many academics have looked into using back-translation (bt) as an alternative to using monolingual data @xcite @xcite @xcite @xcite . the reported mt systems that exploited monolingual data were already trained using large parallel corpus. for low-resource languages like manipuri, back-translation has found to be attempted by a few @xcite @xcite . however, there have been reports of noisy data issues that require post-processing which makes the work less viable (singh and singh, 2020). @xcite showed that low resource language pairs can also be improved with synthetic data where the source is simply a copy of the monolingual target data. @xcite ; @xcite ; @xcite tried to implement iterative procedure that enhances the back-translation and final systems' quality over time. ## experimentation experiments were carried out to get the best translation accuracy for the mni to en translation. figure 1 summarizes and illustrates the final overall system architecture. the methodologies used to construct the nmt systems can be divided into three main categories, which are covered in the subsections that follow: 1) data preparation for nmt, 2) building optimal nmt model and 3) taking advantage of monolingual data. ## result and analysis each step of the experiment conducted was evaluated using the evaluation method explained in 3.4. the effects of different experiments conducted are discussed separately as below. ## conclusion the research work has studied various methodologies like subword tokenization, parameter tuning, iterative back translation using embeddings based sentence similarity score for the transformer based nmt model in lowresource setting involving agglutinative manipuri language. the impacts of each methodology and their advantages and disadvantages are discussed. the work helped achieve a bleu score of 28.17 for mni to en translation in a strictly constrained low-resource environment of lrilt task, wmt-23. the work also exposed several areas for future research. tokenization for manipuri should be studied further. apart from bpe, other subword tokenization methods may be studied and experimented. the effect of iterative back-translation is needed to be studied along with better performing nmt models which are already trained with more parallel sentences.