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Improving BERT-based Query-by-Document Retrieval with Multi-task Optimization Read chapter Read first chapter

2022 | OriginalPaper | Chapter

Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex Information Needs

Authors : Hanane Djeddal, Thomas Gerald, Laure Soulier, Karen Pinel-Sauvagnat, Lynda Tamine

Published in: Advances in Information Retrieval

Publisher: Springer International Publishing

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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.

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previous chapter Leveraging Content-Style Item Representation for Visual Recommendation
next chapter Temporal Event Reasoning Using Multi-source Auxiliary Learning Objectives
Footnotes
1
The large train set for training, and the Y1 benchmark test set for testing.
 
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Metadata
Title
Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex Information Needs
Authors
Hanane Djeddal
Thomas Gerald
Laure Soulier
Karen Pinel-Sauvagnat
Lynda Tamine
Copyright Year
2022
Book
Advances in Information Retrieval

Print ISBN: 978-3-030-99738-0

Electronic ISBN: 978-3-030-99739-7

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-030-99739-7_11