Question rewriting? Assessing its importance for conversational question answering
Abstract
In conversational question answering, systems must correctly interpret the interconnected interactions and generate knowledgeable answers, which may require the retrieval of relevant information from a background repository. Recent approaches to this problem leverage neural language models, although different alternatives can be considered in terms of modules for (a) representing user questions in context, (b) retrieving the relevant background information, and (c) generating the answer. This work presents a conversational question answering system designed specifically for the Search-Oriented Conversational AI (SCAI) shared task, and reports on a detailed analysis of its question rewriting module. In particular, we considered different variations of the question rewriting module to evaluate the influence on the subsequent components, and performed a careful analysis of the results obtained with the best system configuration. Our system achieved the best performance in the shared task and our analysis emphasizes the importance of the conversation context representation for the overall system performance.
Cite
@article{arxiv.2201.09146,
title = {Question rewriting? Assessing its importance for conversational question answering},
author = {Gonçalo Raposo and Rui Ribeiro and Bruno Martins and Luísa Coheur},
journal= {arXiv preprint arXiv:2201.09146},
year = {2022}
}
Comments
Submitted manuscript (not anonymized) accepted to the 44th European Conference on Information Retrieval (ECIR) 2022. This preprint has not undergone peer review (when applicable) or any post-submission improvements or corrections. The Version of Record of this contribution is published in Advances in Information Retrieval, and is available online at https://doi.org/10.1007/978-3-030-99739-7_23