conference-paper Open access

ELI5: Long Form Question Answering

Research footprint

At a glance

Citations
314
References
31
Comments
0
Paper overview

Öz

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.

Record transparency

Publication details

DOI
10.18653/v1/p19-1346
OpenAlex
W2950681488
Document type
conference-paper
Language
EN
Last metadata update
Community

Comments

Oturum Açın to join the discussion.

  1. No comments yet. Start the discussion.