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( 2019) is shown to be capable of modeling the dialog pipeline in a unified way. On the other hand, the large pre-trained language model GPT-2 Radford et al. Visualization and a case study to illustrate the advantages of UBAR in modeling The transfer ability of UBAR to new domains with limited data and provide Thorough analyses demonstrate that the session-level training sequenceįormulation and the generated dialog context are essential for UBAR to operateĪs a fully end-to-end task-oriented dialog system in real life. Optimization, and end-to-end modeling by 4.7, 3.5, and 9.4 points respectively. Multiple settings, improving the combined score of response generation, policy The MultiWOZ datasets show that UBAR achieves state-of-the-art performances in Additionally, UBAR is evaluated in a more realistic setting, where itsĭialog context has access to user utterances and all content it generated suchĪs belief states, system acts, and system responses. Sequence of the entire dialog session which is composed of user utterance,īelief state, database result, system act, and system response of every dialog Specifically, UBAR is acquiredīy fine-tuning the large pre-trained unidirectional language model GPT-2 on the Task-oriented dialogs on a dialog session level. This paper presents our task-oriented dialog system UBAR which models