The U niversity of E dinburgh’s Submission to the WMT 22 Code-Mixing Shared Task ( M ix MT ) (2022)

Abstract: The University of Edinburgh participated in the WMT22 shared task on code-mixed translation. This consists of two subtasks: i) generating code-mixed Hindi/English (Hinglish) text generation from parallel Hindi and English sentences and ii) machine translation from Hinglish to English. As both subtasks are considered low-resource, we focused our efforts on careful data generation and curation, especially the use of backtranslation from monolingual resources. For subtask 1 we explored the effects of constrained decoding on English and transliterated subwords in order to produce Hinglish. For subtask 2, we investigated different pretraining techniques, namely comparing simple initialisation from existing machine translation models and aligned augmentation. For both subtasks, we found that our baseline systems worked best. Our systems for both subtasks were one of the overall top-performing submissions.

## introduction code-mixing is the shift from one language to another within a single conversation or utterance @xcite . it is an extremely common and diverse communicative phenomenon worldwide @xcite , though one which is currently under-served by many nlp technologies @xcite . one of the most well-known examples of codemixing is between hindi and english, commonly referred to as hinglish foot_0 . it is extremely common amongst hindi-english bilingual speakers in both speech and text, used across a range of genres and media @xcite , and has its own distinctive features and linguistic forms @xcite . the process of generating hinglish from the written text is non-trivial, as code-mixing may happen at the phrase or word level, but hindi and english differ substantially syntactically. as a novel addition to the current code-mixing nlp research, we investigated lexically constraining the hinglish output in subtask 1 to only contain words from english and hindi sources. through analysis, we demonstrated that transliteration mismatches could affect performance. another novel approach we explore for this task, particularly for subtask 2, is a denoising-based pretraining technique called aligned augmentation (aa) @xcite . aa, which trains mt models to denoise artificially generated code-mixed text, was shown by @xcite to boost translation performance across a variety of languages -thanks to the enhanced transfer learning brought about by code-mixed pretraining. in this work, we explored if this general-purpose approach could be useful for translating authentic, human-generated code-mixed text, focusing on hinglish. despite these efforts, we found that for both subtasks our original baselines worked better and constituted our final submissions for this task, which ranked as one of the top-performing systems for both subtasks, by both automatic and human evaluation. we hope our methods, particularly hinglish data generation, that allowed us to build these systems would be useful to the community; as would the findings from our additional research explorations. ## subtask 1: translating into hinglish good quality hinglish data is hard to come by, and parallel hinglish data with hindi or english even more frugal. therefore, for both subtasks we concentrated our efforts on generating good hinglish backtranslation. we planned to use the model which produced the highest quality hinglish for subtask 1 as our backtranslator for subtask 2, hence we focused our efforts on each subtask sequentially. ## test results the final test results for our submissions are listed in ## conclusion in this work, we described our various findings and experiences while building nmt systems that translated between hinglish and monolingual english/hindi -as part of the wmt22 code-mixing shared task. we proposed various corpora that could be useful for these tasks -many of which 10 https://tinyurl.com/codalab-ldbd 11 https://tinyurl.com/heval-mixmt we create as part of this work -and utilizing these, build high-performing mt systems that, for both subtasks, constituted one of the leading unconstrained models. in addition, we also explored and analysed some alternative approaches for training our models like constrained decoding and aligned augmentation (aa) which, despite not beating our original baselines, yielded findings that are useful for future research. perhaps the most notable of these suggests that efforts to create hinglish datasets, including using transliterated hindi as an approximation, can be fruitful and pivotal to high performance. while efforts to handle noise in social media text (such as aa-based pretraining) can also help, further research is required to establish the most optimal ways to do the same.