2021/12/08 by Hanane Djeddal, Djeddal, Hanane, Thomas Gerald +7
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Natural Language Processing Techniques #Speech and dialogue systems #Topic Modeling #cs.CL #cs.IR #cs.LG
paper · pdf · doi:10.48550/arxiv.2112.04344
8 pages, 1 figure, ECIR 2022 short paper
arxiv created 2021/12/08 · openalex publication_date 2021/12/08 · arxiv updated 2021/12/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.