@Article{Fan2019,
author = {Fan, Angela and Jernite, Yacine and Perez, Ethan and Grangier, David and Weston, Jason and Auli, Michael}, 
title = {ELI5: Long Form Question Answering}, 
journal = {arXiv}, 
volume = {}, 
number = {}, 
pages = {1907.09190v1}, 
year = {2019}, 
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.}, 
location = {}, 
keywords = {}}

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