ELI5: Long Form Question Answering

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.

References in corpus (4)

• Bidirectional Attention Flow for Machine Comprehension
• A Deep Reinforced Model for Abstractive Summarization
• SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine
• HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

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• Improving Conditioning in Context-Aware Sequence to Sequence Models
• Human Evaluation of Spoken vs. Visual Explanations for Open-Domain QA
• Identifying Risk of Opioid Use Disorder for Patients Taking Opioid Medications with Deep Learning
• Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs