Code-switching input for machine translation: a case study of Vietnamese-English data

Multilingualism presents both a challenge and an opportunity for Natural Language Processing, with code-switching representing a particularly interesting problem for computational models trained on monolingual datasets. In this paper, we explore how code-switched data affects the task of Machine Translation, a task which only recently has started to tackle the challenge of multilingual data. We test three Machine Translation systems on data from the Canberra Vietnamese-English Codeswitching Natural Speech Corpus (CanVEC) and evaluate translation output using both automatic and human metrics. We find that, perhaps counter-intuitively, Machine Translation performs better on code-switching input than monolingual input. In particular, comparison of human and automatic evaluation suggests that codeswitching input may boost the semantic faithfulness of the translation output, an effect we term lexico-semantic enrichment. We also report two cases where this effect is most and least clear in Vietnamese-English, namely gender-neutral 3SG pronouns and interrogative constructions respectively. Overall, we suggest that Machine Translation, and Natural Language Processing more generally, ought to view multilingualism as an opportunity rather than an obstacle.