Bridging the Gap Between Relevance Matching and Semantic Matching for Short Text Similarity Modeling

https://cs.uwaterloo.ca/~jimmylin/publications/Rao_etal_EMNLP2019.pdf

2 HCAN: Hybrid Co-Attention Network

three major components: (1) a hybrid encoder (2) a relevance matching module (3) a semantic matching module

2.1 Hybrid Encoders

hybrid encoder module that explores three types of encoders: deep, wide, and contextual

query and context words :$\{w_1^q,w_2^q,…,w_n^q\},\{w_1^c,w_2^c,…,w_m^c\}$, embedding representations $\textbf{Q}\in \mathbb{R}^{n\times L},\textbf{C}\in \mathbb{R}^{m\times L}$

Deep Encoder

$\textbf{U}$表示$\textbf{Q},\textbf{C}$

Wide Encoder

Unlike the deep encoder that stacks multiple convolutional layers hierarchically, the wide encoder organizes convolutional layers in parallel, with each convolutional layer having a different window size k

Contextual Encoder

2.2 Relevance Matching

2.3 Semantic Matching

2.4 Final Classification