# Instruction Clarification Requests in Multimodal Collaborative Dialogue Games: Tasks, and an Analysis of the CoDraw Dataset

## TL;DR

This paper analyzes instruction clarification requests in multimodal dialogue games, annotates a large dataset, and develops baseline models for predicting and recognizing clarification requests to improve understanding in collaborative AI systems.

## Contribution

It introduces a large annotated corpus of clarification requests in multimodal dialogue and provides formalization and baseline models for key tasks related to clarification detection.

## Key findings

- Large corpus of 8.8k iCRs in 9.9k dialogues
- Demonstrates diversity of clarification requests
- Baseline models show learnability of iCR detection tasks

## Abstract

In visual instruction-following dialogue games, players can engage in repair mechanisms in face of an ambiguous or underspecified instruction that cannot be fully mapped to actions in the world. In this work, we annotate Instruction Clarification Requests (iCRs) in CoDraw, an existing dataset of interactions in a multimodal collaborative dialogue game. We show that it contains lexically and semantically diverse iCRs being produced self-motivatedly by players deciding to clarify in order to solve the task successfully. With 8.8k iCRs found in 9.9k dialogues, CoDraw-iCR (v1) is a large spontaneous iCR corpus, making it a valuable resource for data-driven research on clarification in dialogue. We then formalise and provide baseline models for two tasks: Determining when to make an iCR and how to recognise them, in order to investigate to what extent these tasks are learnable from data.