Los puntos clave no están disponibles para este artículo en este momento.
We introduce the first large-scale corpus for long-form question answering, a task requiring elaborate and in-depth answers to openended questions. The dataset comprises 270K threads from the Reddit forum "Explain Like I'm Five" (ELI5) where an online community provides answers to questions which are comprehensible by five year olds. Compared to existing datasets, ELI5 comprises diverse questions requiring multi-sentence answers. We provide a large set of web documents to help answer the question. Automatic and human evaluations show that an abstractive model trained with a multi-task objective outperforms conventional Seq2Seq, language modeling, as well as a strong extractive baseline. However, our best model is still far from human performance since raters prefer gold responses in over 86% of cases, leaving ample opportunity for future improvement.
Building similarity graph...
Analyzing shared references across papers
Loading...
Angela Fan
Tongji University
Yacine Jernite
Johns Hopkins University
Ethan Perez
Supélec
Google (United States)
Meta (Israel)
Laboratoire Lorrain de Recherche en Informatique et ses Applications
Building similarity graph...
Analyzing shared references across papers
Loading...
Fan et al. (Tue,) studied this question.
synapsesocial.com/papers/69d83cd48c03fbaff8bee662 — DOI: https://doi.org/10.18653/v1/p19-1346