What Do You Mean `Why?': Resolving Sluices in Conversations

TL;DR

This paper introduces the task of resolving sluices, or one-word questions like 'Why?', in conversations, providing a new dataset and baseline models to improve computational understanding of such queries.

Contribution

It presents the first dataset and baseline models for the novel task of sluice resolution in conversational AI.

Findings

Crowd-sourced annotations for over 4,000 dialogues.

Baseline architectures demonstrate the challenge of sluice resolution.

The dataset enables future research in conversational understanding.

Abstract

In conversation, we often ask one-word questions such as `Why?' or `Who?'. Such questions are typically easy for humans to answer, but can be hard for computers, because their resolution requires retrieving both the right semantic frames and the right arguments from context. This paper introduces the novel ellipsis resolution task of resolving such one-word questions, referred to as sluices in linguistics. We present a crowd-sourced dataset containing annotations of sluices from over 4,000 dialogues collected from conversational QA datasets, as well as a series of strong baseline architectures.