M anipuri- E nglish Machine Translation using Comparable Corpus (2021)

Abstract: Unsupervised Machine Translation (MT) model, which has the ability to perform MT without parallel sentences using comparable corpora, is becoming a promising approach for developing MT in low-resource languages. However, majority of the studies in unsupervised MT have considered resource-rich language pairs with similar linguistic characteristics. In this paper, we investigate the effectiveness of unsupervised MT models over a Manipuri-English comparable corpus. Manipuri is a low-resource language having different linguistic characteristics from that of English. This paper focuses on identifying challenges in building unsupervised MT models over the comparable corpus. From various experimental observations, it is evident that the development of MT over comparable corpus using unsupervised methods is feasible. Further, the paper also identifies future directions of developing effective MT for Manipuri-English language pair under unsupervised scenarios.

## introduction the performances of standard data-driven mt systems rely heavily on parallel sentences. unfortunately, parallel resources are not readily available for most low-resource languages and specialized domains, as their generation is a very costly and timeconsuming task. manipuri 1 , is a language spoken in the north-eastern states of india that lacks readily available large parallel sentences. recently developed unsupervised mt models, called unsupervised statistical machine translation (usmt) @xcite and unsupervised neural machine translation (unmt) @xcite , achieved remarkable results without using any parallel sentences. the ability to learn translation features without using parallel data will boost the progress of low-resource mt studies. despite the reported successes, the capability of the unsupervised mt to an actual low-resource scenario is still in question. majorities of the previous unsupervised mtrelated studies @xcite @xcite are for combinations of high resource languages like english, german, french, etc. for which conventional mt works well and where quality monolingual corpora are also available in abundance. studies in @xcite have also reported that usmt and unmt performances usually vary based on the similarity/difference of the source and the target language characteristics like quantity and quality of bilingual corpus, language branch, alphabet, morphology, etc. not only manipuri lacks a large-quality monolingual corpus, but the language is also highly agglutinative. it belongs to the tibeto-burman language group @xcite and has a very complex morphological structure that is very different from english @xcite . the previous study related to unsupervised manipuri-english mt has only exploited unmt models @xcite . however, when considering resource-scarce languages, statistical machine translation (smt) generally outperforms neural machine translation (nmt) @xcite . motivated by the above reason, investigating the performances of both the usmt and unmt models on the distant language pair is meaningful and challenging. to the best of our knowledge, this study is the first attempt to investigate the performance of the usmt model on manipuri language. empirical evaluation of the previous models show that usmt model outperforms unmt models for the language pair. monoses @xcite , a popular usmt model, achieve the best bleu score, followed by the unmt model proposed in @xcite . however, more advance unmt models, mass @xcite and xlm @xcite , fails miserably. although the preliminary experimental results are encouraging, we observe that the direct adaptation of unsupervised mt methods on the language pair is associated with many critical issues. this study also provides an in-depth analysis of the previous usmt and unmt models and investigates their strengths and weaknesses on the language pair. furthermore, we also propose approaches that further improve the translation performance by (1) suffix segmenting manipuri texts to alleviate the data sparsity due to its agglutinating nature, (2) weakly supervising the cross-lingual embeddings generation using transliteration pairs, and (3) generating phrase-table using transliteration models. the rest of the paper is organized as follows. section 2 discusses the related works. section 3.1 and 3.2 provides a detailed description of the usmt and unmt models respectively. section 4 describe the proposed approaches. our experimental setups are presented in the section 5 followed by the results and discussion in section 6. section 8 conclude the study. ## related studies the majority of the previous studies that try to overcome the parallel sentences dependency problem exploited the monolingual data to enhanced the mt system trained on a few hundred thousand parallel sentences @xcite @xcite . as a result, apart from a few @xcite @xcite , mt studies for low-resource manipur-english language pair is still in their inception. there are only a few thousands publicly available manipuri-english parallel sentences @xcite @xcite , which are not sufficient for statistically motivated approaches. unsupervised mt has recently attracted lots of attention because of its ability to learn mt features from abundantly available non-parallel corpora. unsupervised mt is motivated by the successes of word translation models developed based on the unsupervised cross-lingual embedding (ucle) @xcite . ucle forms the core of the unsupervised mt frameworks and is used for initializing the mt model. in this study, we systematically investigate whether the unsupervised mt methods apply to the distant manipuri-english pair. unsupervised mt can be approached by following either the smt or nmt techniques. usmt @xcite follows the modular design comprising several models, whereas the unmt methods @xcite focus on training an end-to-end model. each approach has its merits and demerits. a detailed description of the models is presented in the subsequent sections. in the case of manipuri unsupervised mt, to the best of our knowledge, study in @xcite is the only available literature. the authors developed a unmt for the manipuri-english pair based on transformer with a shared encoder and language-specific decoders. they enhance the model by using a denoising autoencoder followed by a back-translation process, similar to the settings presented in @xcite . the models are fine-tuned using a few parallel sentences as a development set. however, in our study, we do not use any parallel sentences as we want to assess the applicability of the fully unsupervised models on the language pair. ## unsupervised mt models this paper considers the following state-of-the-art unsupervised mt models. ## proposed approaches for handling low-resource scenarios majority of the studies considers bilingual dictionary between the target language pairs to generate cross-lingual embeddings @xcite . under a low-resource scenario, we may assume unavailability of such external resources. motivated by this, this study exploits transliteration pairs of named-entities in place of bilingual dictionaries. the transliteration of named-entities is obtained using method proposed in @xcite . ## results and discussion ## future research directions it is observed from the above observation that there is a potential for developing mt system for manipuri-english language pair using comparable corpora, and may be a way forward to counter the challenges of creating sentence level parallel corpora. however, for developing such a system, we would need effective multi-lingual embedding techniques to develop effective bilingual dictionary, phrase-table, language modelling for post processing sentence correction etc. further, we would also need to take care the dynamic writing styles followed in manipuri. for instance, (জনু ৱারী, জনু ৱাির, জানু ৱারী and জানু ৱাির) are acceptable writing forms of the word january. such a variation is inevitable for comparable corpora while the text are pooled specially from different sources. in addition, from the p 1 performance in ## conclusion we develop a mt system for low-resource distant manipuri-english language pair without using parallel sentences. our study reveals that a relatively cheaper domain-aligned comparable corpora befit potential replacement of expensive parallel sentences for the language pair mt task. we also compare a popular usmt model with state-of-the-art unmt models and found that the modular design of the usmt model is better suited for the language pair. furthermore, this paper empirically shows that using a manipuri suffix segmenter reduces the data sparseness issue due to the manipuri text's agglutinative nature. also, we found that weakly-supervising the usmt model using the transliteration pairs and transliteration models improves the translation performance. though not with high performance, this work provides a stable mt baseline for the low-resource manipuri-english language pair. we also offer several directions for future studies to encourage more research on this crucial problem.