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Conference papers
Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex Information Needs
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.
https://hal.sorbonne-universite.fr/hal-03563311
Contributor : Laure Soulier Connect in order to contact the contributor
Submitted on : Wednesday, February 9, 2022 - 3:58:51 PM
Last modification on : Monday, June 27, 2022 - 12:30:28 PM
Long-term archiving on: : Tuesday, May 10, 2022 - 7:04:20 PM
Contributor : Laure Soulier Connect in order to contact the contributor
Submitted on : Wednesday, February 9, 2022 - 3:58:51 PM
Last modification on : Monday, June 27, 2022 - 12:30:28 PM
Long-term archiving on: : Tuesday, May 10, 2022 - 7:04:20 PM
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