Intent Detection for code-mix utterances in task oriented dialogue systems
Pratik Jayarao, Aman Kumar Srivastava · 2018 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT) · 2018
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 representation for user utterances have been explored for the same. However, these models and vectorization approaches have more so been evaluated in a single language environment. Dialogue systems generally have to deal with queries in different languages and most importantly Code-Mix form of writing. Since Code-Mix texts are not bounded by a formal structure they are difficult to handle. We thus conduct experiments across combinations of models and various vector 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 dataset consisting of only Code-Mix utterances and the other dataset consisting of English, Hindi, and Code-Mix(English-Hindi) utterances.