UBAR: Towards Fully End-to-End Task-Oriented Dialog Systems with GPT-2

TL;DR

UBAR leverages GPT-2 fine-tuning on entire dialog sessions to create a fully end-to-end task-oriented dialog system that outperforms previous models on the MultiWOZ dataset.

Contribution

This work introduces a session-level training approach for task-oriented dialogs using GPT-2, enabling more realistic and effective end-to-end dialog modeling.

Findings

Achieves state-of-the-art results on MultiWOZ datasets.

Improves response generation, policy, and end-to-end scores significantly.

Demonstrates effective transfer to new domains with limited data.

Abstract

This paper presents our task-oriented dialog system UBAR which models task-oriented dialogs on a dialog session level. Specifically, UBAR is acquired by fine-tuning the large pre-trained unidirectional language model GPT-2 on the sequence of the entire dialog session which is composed of user utterance, belief state, database result, system act, and system response of every dialog turn. Additionally, UBAR is evaluated in a more realistic setting, where its dialog context has access to user utterances and all content it generated such as belief states, system acts, and system responses. Experimental results on the MultiWOZ datasets show that UBAR achieves state-of-the-art performances in multiple settings, improving the combined score of response generation, policy optimization, and end-to-end modeling by 4.7, 3.5, and 9.4 points respectively. Thorough analyses demonstrate that the session-level training sequence formulation and the generated dialog context are essential for UBAR to operate as a fully end-to-end task-oriented dialog system in real life. We also examine the transfer ability of UBAR to new domains with limited data and provide visualization and a case study to illustrate the advantages of UBAR in modeling on a dialog session level.