Abstract
One of the major challenges to build a task-oriented dialogue system is that dialogue state transition frequently happens between multiple domains such as booking hotels or restaurants. Recently, the encoderdecoder model based on the end-to-end neural network has become an attractive approach to meet this challenge. However, it usually requires a sufficiently large amount of training data and it is not flexible to handle dialogue state transition. This paper addresses these problems by proposing a simple but practical framework called Multi-Domain KB-BOT (MDKB-BOT), which leverages both neural networks and rule-based strategy in natural language understanding (NLU) and dialogue management (DM). Experiments on the data set of the Chinese Human-Computer Dialogue Technology Evaluation Campaign show that MDKB-BOT achieves competitive performance on several evaluation metrics, including task completion rate and user satisfaction.
Highlights
In the past decade, dialogue systems have become an attractive topic and they can be classified into open-domain dialogue systems and task-oriented dialogue systems
The encoderdecoder model based on the end-to-end neural network has become an attractive approach to meet this challenge. It usually requires a sufficiently large amount of training data and it is not flexible to handle dialogue state transition. This paper addresses these problems by proposing a simple but practical framework called Multi-Domain KB-BOT (MDKB-BOT), which leverages both neural networks and rulebased strategy in natural language understanding (NLU) and dialogue management (DM)
We run the evaluation experiments on the data set of the Chinese Human-Computer Dialogue Technology Evaluation Campaign and experimental results show that MDKB-BOT can robustly fulfill the frequent changes of user intent among three domains and achieve competitive scores based on human evaluation metrics
Summary
Dialogue systems have become an attractive topic and they can be classified into open-domain dialogue systems and task-oriented dialogue systems. Two major drawbacks of those systems are that multiple training corpora are required and generic responses such as “I do not know” are likely to be generated These drawbacks limit the generalization ability, especially for a task-oriented system in which knowledge from multiple domains is needed to understand users’ underlying intents. Each essential component is trained individually, including 1) Natural Language Understanding (NLU), to specify task domain and user intent and extract slot-value pairs, 2) Dialogue Manager (DM), to keep tracking the dialogue state and guide users to achieve a desired goal, and 3) Natural Language Generation (NLG), to generate responses. To address the complex dialogue state transition problem, we adopt the architecture of modularized pipeline and propose a multi-domain KB-BOT (MDKB-BOT), which leverages both rule extraction and neural networks. We run the evaluation experiments on the data set of the Chinese Human-Computer Dialogue Technology Evaluation Campaign and experimental results show that MDKB-BOT can robustly fulfill the frequent changes of user intent among three domains (flight, train and hotel) and achieve competitive scores based on human evaluation metrics
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