Abstract
Answering complex questions from long documents requires aggregating multiple pieces of evidence and then predicting the answers. In this paper, we propose a multi-hop retrieval method, DocHopper, to answer compositional questions over long documents. At each step, DocHopper retrieves a paragraph or sentence embedding from the document, mixes the retrieved result with the query, and updates the query for the next step. In contrast to many other retrieval-based methods (e.g., RAG or REALM) the query is not augmented with a token sequence: instead, it is augmented by "numerically" combining it with another neural representation. This means that model is end-to-end differentiable. We demonstrate that utilizing document structure in this was can largely improve question-answering and retrieval performance on long documents. We experimented with DocHopper on three different QA tasks that require reading long documents to answer compositional questions: discourse entailment reasoning, factual QA with table and text, and information seeking QA from academic papers. DocHopper outperforms all baseline models and achieves state-of-the-art results on all datasets. Additionally, DocHopper is efficient at inference time, being 3~10 times faster than the baselines.
Abstract (translated)
URL
https://arxiv.org/abs/2106.00200
