Abstract
Scenario-based question answering is a trending research topic in artificial intelligence. While existing efforts are focused on multiple-choice questions, in this article, we consider scenario-based essay question answering (SEQA). This task requires generating a paragraph-long answer from both the scenario description that contextualizes the question and from external knowledge. To solve the task, our approach sentence ranking, reasoning, and replication (SR3) selects top-ranked sentences from the scenario and external knowledge to feed a novel sequence-to-sequence model, which enhances the synergy between sentences by reasoning over a sentence graph and then generates an answer with a new sentence-level copying mechanism. SR3 significantly outperforms a variety of strong baseline methods on GeoSEQA, a dataset containing 4003 scenario-based essay questions collected from China’s high-school geography exams.
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