CNLP - NITS - PP at M ix MT 2022: H inglish- E nglish Code-Mixed Machine Translation (2022)

Abstract: The mixing of two or more languages in speech or text is known as code-mixing. In this form of communication, users mix words and phrases from multiple languages. Code-mixing is very common in the context of Indian languages due to the presence of multilingual societies. The probability of the existence of code-mixed sentences in almost all Indian languages since in India English is the dominant language for social media textual communication platforms. We have participated in the WMT22 shared task of code-mixed machine translation with the team name: CNLP-NITS-PP. In this task, we have prepared a synthetic Hinglish–English parallel corpus using transliteration of original Hindi sentences to tackle the limitation of the parallel corpus, where, we mainly considered sentences that have named-entity (proper noun) from the available English-Hindi parallel corpus. With the addition of synthetic bi-text data to the original parallel corpus (train set), our transformer-based neural machine translation models have attained recall-oriented understudy for gisting evaluation (ROUGE-L) scores of 0.23815, 0.33729, and word error rate (WER) scores of 0.95458, 0.88451 at Sub-Task-1 (English-to-Hinglish) and Sub-Task-2 (Hinglish-to-English) for test set results respectively.

## introduction the mixing of alternating words from two different language vocabulary without misinterpreting the context of the sentence is known as code-switching or code-mixing @xcite . this style of communication is one of the most frequent in multilingual communities, such as india. english is extensively mixed with local languages, such as hindi, and bengali, which causes code-mixed english-hindi: hinglish and english-bengali: binglish languages @xcite . code-mixing is not observed in formal literature such as books but is commonly used on social media platforms such as face-book and twitter. the wmt22 organizes shared task code-mixed machine translation for englishto-hinglish and hinglish-to-english, where the main challenge is low-resource availability of parallel corpus. we have participated in the same task and to mitigate the issue of data scarcity, a synthetic hinglish-english parallel corpus is prepared (as discussed in section 3.1). in this work, the transformer-based neural machine translation (nmt) technique @xcite is utilized to build nmt models for both directions (english-to-hinglish, hinglish-to-english) of code-mixed mt. ## related work in recent times, many significant nlp studies have included the study of code-mixed languages. the emnlp 2022 seventh conference on machine translation (wmt22) has put forward several tasks directed to meet new challenges in the field of nlp for code-mixed indian languages. the competition has attracted many researchers to follow up with these tasks, which have eventually led to new directions and problems in this domain. the task of machine translation for code-mixed languages has not been an active area of research due to the scarcity of manually annotated datasets. recently, researchers have been developing datasets for codemixed mt that includes hinglish-english parallel corpus, namely, hinge @xcite and phinc @xcite to overcome the datasets scarcity issue to build codemix mt that is associated with the code-mixed text from various social media platforms. in this work, we addressed the issue of data scarcity by using synthetic hinglish-english parallel corpus to increase the training data for code-mixed mt shared tasks at wmt22. ## system description the experiments are carried out in four phases, namely, synthetic data preparation and augmentation to the train set, data preprocessing, model training, and testing. the opennmt-py @xcite tool is utilized to build the nmt models independently for english-to-hinglish (subtask-1) and hinglish-to-english (subtask-2). ## results the wmt22 shared task organizer published the evaluation result foot_3 of the code-mixed machine translation (mixmt) task for english-hinglish language pair. we participated with the team name cnlp-nits-pp in the monolingual to code-mixed machine translation: english-to-hinglish (sub-task-1) and code-mixed to a monolingual machine translation: hinglish-to-english (sub-task-2) submission tracks of the same task where ten teams participated. the automatic evaluation metrics, namely, rouge (recall-oriented understudy for gisting evaluation) @xcite , wer (word error rate) @xcite and human evaluation (he) are used for the evaluation of results. ## conclusion and future work in this work, we have investigated a transformerbased model for hinglish-english language pair in the wmt22 code-mixed mt task. we have addressed the data scarcity issue by the augmentation of synthetic hinglish-english parallel sentences to the train set for both english-to-hinglish and hinglish-to-english translation tasks (sub-task-1 and sub-task-2). furthermore, synthetic parallel data will be corrected in the future to improve translational performance.