There is a growing interest in developing goal-oriented dialog systems which serve users in accomplishing complex tasks through multi-turn conversations.
This work introduces the Generative Conversation Control model, an augmented and fine-tuned GPT-2 language model that conditions on past reference conversations to probabilistically model multi-turn conversations in the actor's persona.
To this end, we construct a multilingual NLU corpus, MultiATIS++, by extending the Multilingual ATIS corpus to nine languages across various language families.
We found that users appreciated aspects of each system (e. g., voice-input efficiency, text-input precision) and that novice users were more optimistic about programming using voice-input than advanced users.
The noetic end-to-end response selection challenge as one track in the 7th Dialog System Technology Challenges (DSTC7) aims to push the state of the art of utterance classification for real world goal-oriented dialog systems, for which participants need to select the correct next utterances from a set of candidates for the multi-turn context.
Goal-oriented dialog systems, which can be trained end-to-end without manually encoding domain-specific features, show tremendous promise in the customer support use-case e. g. flight booking, hotel reservation, technical support, student advising etc.
In goal-oriented dialog systems, belief trackers estimate the probability distribution of slot-values at every dialog turn.
Neural network models have recently gained traction for sentence-level intent classification and token-based slot-label identification.
In this paper we introduce Chat-crowd, an interactive environment for visual layout composition via conversational interactions.