用关系抽取和知识图谱匹配来解答选择题，还能追踪推理过程。
Applying Relation Extraction and Graph Matching to Answering Multiple Choice Questions
- 用Transformer提取关系构建动态知识图谱，实现可追溯的答题。
- 在封闭世界假设下验证句子真伪，正确率可达约70%。
- 适合需要可解释性的问答任务，对题目类别敏感。
本研究将基于Transformer的关系抽取与知识图谱（KG）匹配相结合，用于解答多项选择题（MCQs），同时保持输出过程的可追溯性。知识图谱是实体与关系构成的结构化事实知识表示。由于构建成本高，传统上被视为静态数据库。但近期基于Transformer的关系抽取方法使我们能够通过自然语言文本动态生成知识图谱，从而实现输入语句的语义表征。利用这一特性，我们提出一种以填空形式回答多项选择题的方法，并特别关注：当关系抽取模型输入事实错误文本时，会生成虚假信息的问题。为此，我们通过（i）使用关系抽取方法将句子转化为关系图，以及（ii）在封闭世界假设下与真实知识图谱进行验证，来评估每个问题句子的真实性。实验结果表明，该方法正确回答了约70%的问题，且过程可追溯。同时，我们发现题目类别对准确率有显著影响。
原文摘要 · Abstract (English)
In this research, we combine Transformer-based relation extraction with matching of knowledge graphs (KGs) and apply them to answering multiple-choice questions (MCQs) while maintaining the traceability of the output process. KGs are structured representations of factual knowledge consisting of entities and relations. Due to the high construction cost, they had been regarded as static databases with validated links. However, the recent development of Transformer-based relation extraction (RE) methods has enabled us to generate KGs dynamically by giving them natural language texts, and thereby opened the possibility for representing the meaning of the input sentences with the created KGs. Using this effect, we propose a method that answers MCQs in the "fill-in-the-blank" format, taking care of the point that RE methods generate KGs that represent false information if provided with factually incorrect texts. We measure the truthfulness of each question sentence by (i) converting the sentence into a relational graph using an RE method and (ii) verifying it against factually correct KGs under the closed-world assumption. The experimental results demonstrate that our method correctly answers up to around 70% of the questions, while providing traceability of the procedure. We also highlight that the question category has a vast influence on the accuracy.
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