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

本研究旨在评估多语言语境下各种向量表示和模型的组合，找到最适合的方案以实现 Code Mix 话语意图识别。研究结果在 Code Mix 和英汉混合语数据集上得到了验证。