Intent Classification from Code Mixed Input for Virtual Assistants
Siddhartha Mukherjee, Anish Nediyanchath, Abhishek Singh, Vinuthkumar Prasan, Divya Verma Gogoi, Surya Pratap Singh Parmar · 2021
Virtual Assistants like Bixby, Google Assistant, Cortana & Alexa are making life easier by understanding intent from user's input utterance. Adoption of Virtual Assistants in multilingual society like India is harder than in monolingual communities. People in multilingual societies tend to use linguistic elements from multiple languages in a single sentence. This phenomenon produces code-mixing, which lacks standard syntactic & semantic linguistic properties unlike monolingual input. It is a challenging NLP Task to understand user's intent from code-mixed input. There is a lack of relevant corpus & researches for Intent Classification on Code-mixed text. In this paper, we introduce first of its kind a Hindi-English Code-Mixed dataset for Intent Classification (CoMTIC), covering 10 most preferred features for a Virtual Assistant. We also introduce a novel deep learning based method of Intent classification from Code-Mixed Text. We conduct empirical analysis by comparing the suitability & performance with various state-of-the-art methods. Our method attains 96.68% accuracy on our dataset.