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Intent detection is an essential component of task oriented dialogue systems.Over the years, extensive research has been conducted resulting in many stateof the art models directed towards resolving user's intents in dialogue. Avariety of vector representations foruser utterances have been explored for thesame. However, these models and vectorization approaches have more so beenevaluated in a single language environment. Dialogude systems generally have todeal with queries in different languages. We thus conduct experiments acrosscombinations of models and various vectors representations for Code Mix as wellas multi language utterances and evaluate how these models scale to a multilanguage environment. Our aim is to find the best suitable combination ofvector representation and models for the process of intent detection for CodeMix utterances. we have evaluated the experiments on two different datasetsconsisting of only Code Mix utterances and the other dataset consisting ofEnglish, Hindi and Code Mix English Hindi utterances.

Intent Detection for code-mix utterances in task oriented dialogue systems