Deep Bi-Directional LSTM Network for Query Intent Detection

Abstract

Detecting the user intentions encoded in text queries is a pivotal task in many natural language processing application like search engines, personal assistants, smart agents, and robots. Previous works have explored the use of various machine learning algorithms for the task of intent detection from user queries. In this work, we are proposing a deep learning based framework using Bi-Directional Long Short-Term Memory (BLSTM) Networks for intent identification. The proposed model takes word embeddings as input and learns useful features for identifying the possible intentions of a user query. Instead of directly using word embeddings generated using GloVe Model for training the model, a semantically enriched set of embeddings are used to ensure semantic correctness of word embeddings. The evaluation results on ATIS dataset shows that semantic enrichment and proposed deep learning model improves the results of intent detection.