Measuring interpretable semantic similarity of sentences using a multi chunk aligner

摘要

This work focuses on bolstering the pre-existing Interpretable Semantic Textual Similarity (iSTS) method, that will enable a user to understand the behaviour of an artificial intelligent system. The proposed iSTS method explains the similarities and differences between a pair of sentences. The objective of the iSTS problem is to formalize the alignment between a pair of text segments and to label the relationship between the text fragments with a relation type and relatedness score. The overall objective of this work is to develop a 1:M multi chunk aligner for an iSTS method, which is trained on SemEval 2016 Task 2 dataset. The obtained result outperforms many state-of-art aligners, which were part of SemEval 2016 iSTS task.