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You are here: Home / Papers / End-to-End Neural Pipeline for Goal-Oriented Dialogue System using GPT-2

Donghoon Ham*, Jeong-Gwan Lee*, Youngsoo Jang, and Kee-Eung Kim (2020)

End-to-End Neural Pipeline for Goal-Oriented Dialogue System using GPT-2

In: AAAI Conference on Artificial Intelligence (AAAI) DSTC8 Workshop.

The first sub-task in the multi-domain task-completion dialogue challenge track in the 8th dialogue systems technology challenge (DSTC8) requires participants to build an end-to-end dialogue system that is capable of complex multi-domain dialogues. The traditional approach to build such a dialogue system is to take a pipelined architecture, where its modular components are optimized individually. However, such an optimization scheme does not necessarily yield the overall performance improvement of the whole system. On the other hand, most end-to-end dialogue systems with monolithic neural architecture are trained only with input-output utterances, without taking into account the entire annotations available in the corpus. This scheme makes it difficult for goal-oriented dialogues where the system needs to interact with external systems such as database engines or to provide interpretable information about why the system decided to generate a particular response. In this paper, we present an end-to-end neural architecture for dialogue systems that addresses both challenges above. In the official human evaluation, our dialogue system achieved the success rate of 68.32%, the language understanding score of 4.149, and the response appropriateness score of 4.287, which ranked the system at the top position in all performance evaluation criteria.