Most dialog systems posit that users have ﬁgured out clear and speciﬁc goals before starting an interaction. For example, users have determined the departure, the destination, and the travel time for booking a ﬂight. However, in many scenarios, limited by experience and knowledge, users may know what they need, but still struggle to ﬁgure out clear and speciﬁc goals by determining all the necessary slots.

In this paper, we identify this challenge, and make a step forward by collecting a new human-to-human mixed-type dialog corpus. It contains 5k dialog sessions and 168k utterances for 4 dialog types and 5 domains. Within each session, an agent ﬁrst provides user-goalrelated knowledge to help ﬁgure out clear and speciﬁc goals, and then help achieve them.

Furthermore, we propose a mixed-type dialog model with a novel Prompt-based continual learning mechanism. Speciﬁcally, the mechanism enables the model to continually strengthen its ability on any speciﬁc type by utilizing existing dialog corpora effectively.

Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals

Posters: Dialogue and Interactive Systems

findings / work in progress

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ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

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Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals

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