We also provide technical appendix in Appendix.pdf. The advantages of UBAR in modeling on a dialog session
The transfer ability of UBAR to new domains with limitedĭata and provide visualization and a case study to illustrate Thorough analyses demonstrate that the sessionlevel training sequence formulation and the generated dialogĬontext are essential for UBAR to operate as a fully end-toend task-oriented dialog system in real life. The combined score of response generation, policy optimization, and end-to-end modeling by 4.7, 3.5, and 9.4 points respectively. State-of-the-art performances in multiple settings, improving Experimental results on the MultiWOZ datasets show that UBAR achieves States, system acts, and system responses. To user utterances and all content it generated such as belief In a more realistic setting, where its dialog context has access Of the entire dialog session which is composed of user utterance, belief state, database result, system act, and system response of every dialog turn. Specifically, UBAR is acquired by fine-tuning the large pretrained unidirectional language model GPT-2 on the sequence Which models task-oriented dialogs on a dialog session level. This paper presents our task-oriented dialog system UBAR
UBAR PHILLY CODE
This is the code and data for the AAAI 2021 paper "UBAR: Towards Fully End-to-End Task-Oriented Dialog System with GPT-2".
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