Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks
This addresses the challenge of complex QA requiring multi-hop reasoning, but it is incremental as it builds on existing graph-based methods for structured text representations.
The paper tackles the problem of multi-hop question answering, which requires integrating supporting facts from different sources, by proposing a Document Graph Network (DGN) that uses message passing over graph-structured text. The result is competitive performance on HotpotQA compared to a baseline, confirming the relevance of structured representations for this task.
Recent advances in reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text. However, complex Question Answering (QA) typically requires multi-hop reasoning - i.e. the integration of supporting facts from different sources, to infer the correct answer. This paper proposes Document Graph Network (DGN), a message passing architecture for the identification of supporting facts over a graph-structured representation of text. The evaluation on HotpotQA shows that DGN obtains competitive results when compared to a reading comprehension baseline operating on raw text, confirming the relevance of structured representations for supporting multi-hop reasoning.