ELI5: Long Form Question Answering

Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston +1 more
7/22/2019
cs.CL

Official code: facebookresearch/ELI5 (326 stars, last commit 2021-08-31).

Abstract

We introduce the first large-scale corpus for long-form question answering, a task requiring elaborate and in-depth answers to open-ended 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.

View on arXivView PDF

Code Implementations(1)

facebookresearch/ELI5Official100%

Scripts and links to recreate the ELI5 dataset.

32642Shell, PythonFeb 15, 20195 years agoNOASSERTION

Cite this paper

@article{fan2019eli,
  title  = {ELI5: Long Form Question Answering},
  author = {Angela Fan and Yacine Jernite and Ethan Perez and David Grangier and Jason Weston and Michael Auli},
  year   = {2019},
  eprint = {1907.09190},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1907.09190}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/1907.09190.svg)](https://codeofpaper.com/paper/1907.09190)

Discussion