ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)

TL;DR

This paper introduces the ClariQ challenge, a benchmark for evaluating systems that generate clarifying questions in open-domain dialogue, aiming to improve disambiguation in conversational AI.

Contribution

It provides a standardized evaluation framework and organizes a challenge to advance research on clarifying question generation in dialogue systems.

Findings

Established a new benchmark for clarifying questions in dialogue

Conducted offline and human-in-the-loop evaluations

Facilitated progress in mixed-initiative conversational AI

Abstract

This document presents a detailed description of the challenge on clarifying questions for dialogue systems (ClariQ). The challenge is organized as part of the Conversational AI challenge series (ConvAI3) at Search Oriented Conversational AI (SCAI) EMNLP workshop in 2020. The main aim of the conversational systems is to return an appropriate answer in response to the user requests. However, some user requests might be ambiguous. In IR settings such a situation is handled mainly thought the diversification of the search result page. It is however much more challenging in dialogue settings with limited bandwidth. Therefore, in this challenge, we provide a common evaluation framework to evaluate mixed-initiative conversations. Participants are asked to rank clarifying questions in an information-seeking conversations. The challenge is organized in two stages where in Stage 1 we evaluate the submissions in an offline setting and single-turn conversations. Top participants of Stage 1 get the chance to have their model tested by human annotators.