Abstract
Intent detection and slot filling are important modules in task-oriented dialog systems. In order to make full use of the relationship between different modules and resource sharing, solving the problem of a lack of semantics, this paper proposes a multitasking learning intent-detection system, based on the knowledge-base and slot-filling joint model. The approach has been used to share information and rich external utility between intent and slot modules in a three-part process. First, this model obtains shared parameters and features between the two modules based on long short-term memory and convolutional neural networks. Second, a knowledge base is introduced into the model to improve its performance. Finally, a weighted-loss function is built to optimize the joint model. Experimental results demonstrate that our model achieves better performance compared with state-of-the-art algorithms on a benchmark Airline Travel Information System (ATIS) dataset and the Snips dataset. Our joint model achieves state-of-the-art results on the benchmark ATIS dataset with a 1.33% intent-detection accuracy improvement, a 0.94% slot filling F value improvement, and with 0.19% and 0.31% improvements respectively on the Snips dataset.
Highlights
With the development of the task-oriented dialog system, nature language understanding (NLU), as a critical component of the task-oriented dialog system, has attracted great research attention
In order to solve the above problems, we propose a joint model of intent detection and slot filling based on multitask learning (MTL) with a knowledge base, which makes full use of the external knowledge and the high-quality relationship information between intents and slots
In order to completely utilize the incidence relations and shared resources between In order to completely utilize the incidence relations and shared resources between the two modules by only relying on present joint models, and exploring the value of the two modules by only relying on present joint models, and exploring the value of knowledge base to these modules, this paper proposes a joint model for intent detection knowledge base to these modules, this paper proposes a joint model for intent detection and slot filling based on MTL with a knowledge base, which makes full use of the external and slot filling based on MTL with a knowledge base, which makes full use of the external knowledge, and a high-quality relationship information between intents and slots
Summary
With the development of the task-oriented dialog system, nature language understanding (NLU), as a critical component of the task-oriented dialog system, has attracted great research attention. We can capture context information to identify the user’s intent by using intelligent interactive devices that talk to humans in different scenarios, and extracting the semantic constituents from the text that the user inputs into the semantic slots that were previously defined [1]. These two modules, namely intent detection and slot filling, can convert the text into its semantic representation, which provides the task information for supporting the dialog system and helps users achieve their demands. A sentence spoken by a user, such as, “Tell me about the weather” should be classified as a weather-query subtype
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