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The Role of Lexical Alignment in Human Understanding of Explanations by Conversational Agents

<p>This dataset was collected as part of the research investigating the role of lexical alignment in understanding. The dataset consists of the responses of 179 participants recruited via Prolific (a crowdsourcing platform).</p> <p>1. Dialogue with a conversational agent (CA)</p> <p>2. Survey questionnaire responses from the participants.</p> <p>3. Primes selected or provided by the participants.</p> <p>&nbsp;</p> <p>This repository contains:</p> <ol> <li>Cleaned data sources listed above. The data is cleaned by removing data from participants who failed attention checks, or any other factor leading to error such as experiment setup, etc.</li> <li>A Python (Jupyter) playbook to analyse the collected data (Analysis_20230927.ipynb)</li> <li>Experiment design (exp_design_flow.png)</li> </ol>

ShareScore

40/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
4
Access
12
Reuse readiness
8
Engagement
8

Topics

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