September 10, 2019
Bridging the Gap Between Relevance Matching and Semantic Matching for Short Text Similarity Modeling
Conference on Empirical Methods in Natural Language Processing (EMNLP)
We propose a novel model, HCAN (Hybrid Co-Attention Network), that comprises (1) a hybrid encoder module that includes ConvNet-based and LSTM-based encoders, (2) a relevance matching module that measures soft term matches with importance weighting at multiple granularities, and (3) a semantic matching module with co-attention mechanisms that capture context-aware semantic relatedness.
By: Jinfeng Rao, Linqing Liu, Yi Tay, Wei Yang, Peng Shi, Jimmy Lin
Facebook AI Research
Natural Language Processing & Speech