Interpretable semantic textual similarity: Finding and explaining differences between sentences
作者：
Highlights：
• We address interpretability, the ability of machines to explain their reasoning.
• We formalize it for textual similarity as graded typed alignment between 2 sentences.
• We release an annotated dataset and build and evaluate a high performance system.
• We show that the output of the system can be used to produce explanations.
• 2 user studies show preliminary evidence that explanations help humans perform better.
摘要
•We address interpretability, the ability of machines to explain their reasoning.•We formalize it for textual similarity as graded typed alignment between 2 sentences.•We release an annotated dataset and build and evaluate a high performance system.•We show that the output of the system can be used to produce explanations.•2 user studies show preliminary evidence that explanations help humans perform better.
论文关键词：Interpretability,Tutoring systems,Semantic textual similarity,Natural language understanding
论文评审过程：Received 1 March 2016, Revised 9 December 2016, Accepted 11 December 2016, Available online 12 December 2016, Version of Record 25 January 2017.
论文官网地址：https://doi.org/10.1016/j.knosys.2016.12.013
