Intent Detection for code-mix utterances in task oriented dialogue systems
This addresses the problem of handling code-mix queries in dialogue systems for users in multilingual environments, but it is incremental as it applies existing methods to new data.
The paper tackles intent detection for code-mix utterances in task-oriented dialogue systems by evaluating combinations of models and vector representations across multi-language datasets, finding that certain combinations perform best for code-mix data.
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.