MultiGranCNN: An Architecture for General Matching of Text Chunks on Multiple Levels of Granularity

We present MultiGranCNN, a general deep learning architecture for matching text chunks. MultiGranCNN supports multigranular comparability of representations: shorter sequences in one chunk can be directly compared to longer sequences in the other chunk. MultiGranCNN also contains a flexible and modularized match feature component that is easily adaptable to different types of chunk matching. We demonstrate stateof-the-art performance of MultiGranCNN on clause coherence and paraphrase identification tasks.

• Type: Journal
• Year: 2015
• Where: ACL
• Authors: Wenpeng Yin, Hinrich Schütze
• Keywords: ACL 2015, Computational Linguistics