UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification
Andreas Hanselowski†∗, Hao Zhang∗, Zile Li∗, Daniil Sorokin†∗, Benjamin Schiller∗, Claudia Schulz†∗, Iryna Gurevych†∗ †Research Training Group AIPHES Computer Science Department, Technische Universität Darmstadt https://www.aiphes.tu-darmstadt.de ∗Ubiquitous Knowledge Processing Lab (UKP-TUDA) Computer Science Department, Technische Universität Darmstadt https://www.ukp.tu-darmstadt.de/
Abstract
The Fact Extraction and VERification (FEVER) shared task was launched to support the development of systems able to verify claims by extracting supporting or refuting facts from raw text. The shared task organizers provide a large-scale dataset for the consecutive steps involved in claim verification, in particular, document retrieval, fact extraction, and claim classification. In this paper, we present our claim verification pipeline approach, which, according to the preliminary results, scored third in the shared task, out of 23 competing systems. For the document retrieval, we implemented a new entity linking approach. In order to be able to rank candidate facts and classify a claim on the basis of several selected facts, we introduce two extensions to the Enhanced LSTM (ESIM).
中文速览
自动核查网络谣言和假新闻，关键难点在于如何从海量文本中又快又准地找到支持或反驳某个说法的证据。研究团队针对FEVER（事实抽取与验证）评测任务，搭建了一套三段式流水线：先用基于成分句法分析和手工规则的实体链接（entity linking）方法从维基百科中检索相关文档，再用改造后的增强型序列推理模型（ESIM，Enhanced Sequential Inference Model）对候选句子打分排序，最后再次扩展ESIM——引入注意力机制同时处理多条证据句，判断该说法是"被支持""被反驳"还是"信息不足"。在23支参赛队伍中，该系统以第三名完赛，相比官方基线整体性能提升约100%。这项工作表明，将实体链接与深度语义匹配模型结合，是构建可落地的自动事实核查系统的一条有效路径。
原文 arXiv:1809.01479;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1809.01479v5