Natural Language Understanding for Partial Queries

Typical natural language understanding systems are built based on the assumption that they have access to the fully formed complete queries. Today's natural user interfaces, however, enable users to interact with various services and agents (e.g. search engines, personal digital assistants) running on desktop computers and laptops. The system is expected to understand the user's intent while the user is typing the query with the goal of increasing system response rate and ultimately improving the user's productivity. Language understanding models built on fully formed queries perform poorly when tested on partial or incomplete queries. In this study, we consider the problem of domain detection for typed partial natural language queries. We design two sets of features in addition to lexical features to train a multi-valued domain classification model. The first feature set consists of character n-gram features, and the second is the class-based features extracted from clustering of word embeddings. Our experiments show that the two feature sets improve the model's performance by up to 52.8% in comparison to the lexical n-gram baselines.