Semantic Matching from Different Perspectives
This work addresses the need for multi-perspective similarity evaluation in natural language processing, but it is incremental as it builds on existing text matching models without introducing a new method.
The authors tackled the problem of semantic matching by emphasizing that similarity should be assessed from multiple perspectives, and they released a Multi-Perspective Text Similarity (MPTS) dataset with twelve perspectives, providing baseline models and conclusions for future research.
In this paper, we pay attention to the issue which is usually overlooked, i.e., \textit{similarity should be determined from different perspectives}. To explore this issue, we release a Multi-Perspective Text Similarity (MPTS) dataset, in which sentence similarities are labeled from twelve perspectives. Furthermore, we conduct a series of experimental analysis on this task by retrofitting some famous text matching models. Finally, we obtain several conclusions and baseline models, laying the foundation for the following investigation of this issue. The dataset and code are publicly available at Github\footnote{\url{https://github.com/autoliuweijie/MPTS}