Abstract

We will discuss 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. In subsequent work, we propose constructing a local graph structured knowledge base for each query, which compresses the web search information and reduces redundancy. We show that by linearizing the graph into a structured input sequence, models can encode the graph representations within a standard Sequence-to-Sequence setting. We apply this approach to long form question answering. By feeding graph representations as input, we can achieve better performance than using retrieved text portions.

Bio

Angela is a Ph.D. student at INRIA Nancy and Facebook AI Research Paris, advised by Claire Gardent, Chloe Braud, and Antoine Bordes. Before starting her Ph.D., she was a research engineer at FAIR for three years and received a Bachelor's degree in statistics at Harvard. Angela is interested in text generation and more efficient NLP.