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

2021-12-08 15:51:27
Hanane Djeddal, Thomas Gerald, Laure Soulier, Karen Pinel-Sauvagnat, Lynda Tamine
arXiv_CL
     
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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URL

https://arxiv.org/abs/2112.04344

PDF

https://arxiv.org/pdf/2112.04344.pdf

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