A Context-enhanced Adaptive Graph Network for Time-sensitive Question Answering

Abstract

Time-sensitive question answering is to answer questions limited to certain timestamps based on the given long document, which mixes abundant temporal events with an explicit or implicit timestamp. While existing models make great progress in answering time-sensitive questions, their performance degrades dramatically when a long distance separates the correct answer from the timestamp mentioned in the question. In this paper, we propose a Context-enhanced Adaptive Graph network (CoAG) to capture long-distance dependencies between sentences within the extracted question-related episodes. Specifically, we propose a time-aware episode extraction module that obtains question-related context based on timestamps in the question and document. As the involvement of episodes confuses sentences with adjacent timestamps, an adaptive message passing mechanism is designed to capture and transfer inter-sentence differences. In addition, we present a hybrid text encoder to highlight question-related context built on global information. Experimental results show that CoAG significantly improves compared to state-of-the-art models on five benchmarks. Moreover, our model has a noticeable advantage in solving long-distance time-sensitive questions, improving the EM scores by 2.03% to 6.04% on TimeQA-Hard.