CCPE-M (Coached Conversational Preference Elicitation dataset for Movies)

Introduced by Radlinski et al. in Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences

A dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing movie preferences in natural language.

The corpus was constructed from dialogues between two paid crowd-workers using a Wizard-of-Oz methodology. One worker plays the role of an "assistant", while the other plays the role of a "user". The "assistant" is tasked with eliciting the "user" preferences about movies following a Coached Conversational Preference Elicitation (CCPE) methodology. In particular, the assistant is required to ask questions designed so as to minimize the bias in the terminology the "user" employs to convey his or her preferences, and obtain these in as natural language as possible. Each dialog is annotated with entity mentions, preferences expressed about entities, descriptions of entities provided, and other statements of entities.

Source: CCPE-M: Coached Conversational Preference Elicitation dataset for Movies


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