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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...
This paper proposes a new technique to enable Natural Language Understanding (NLU) systems to handle user queries beyond their original semantic schemas defined by intents and slots. Knowledge graph and search query logs are used to extend NLU system's coverage by transferring intents from other domains to a given domain. The transferred intents as well as existing intents are then applied to a set...
Applications of Deep Belief Nets (DBN) to various problems have been the subject of a number of recent studies ranging from image classification and speech recognition to audio classification. In this study we apply DBNs to a natural language understanding problem. The recent surge of activity in this area was largely spurred by the development of a greedy layer–wise pretraining method that uses an...
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