Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex Information Needs
Computation and Language
2021-12-09 v1 Information Retrieval
Machine Learning
Abstract
In this work, our aim is to provide a structured answer in natural language to a complex information need. Particularly, we envision using generative models from the perspective of data-to-text generation. We propose the use of a content selection and planning pipeline which aims at structuring the answer by generating intermediate plans. The experimental evaluation is performed using the TREC Complex Answer Retrieval (CAR) dataset. We evaluate both the generated answer and its corresponding structure and show the effectiveness of planning-based models in comparison to a text-to-text model.
Keywords
Cite
@article{arxiv.2112.04344,
title = {Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex Information Needs},
author = {Hanane Djeddal and Thomas Gerald and Laure Soulier and Karen Pinel-Sauvagnat and Lynda Tamine},
journal= {arXiv preprint arXiv:2112.04344},
year = {2021}
}
Comments
8 pages, 1 figure, ECIR 2022 short paper