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

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Table 1: Sample sentence pairs from TREC Microblog 2013, Quora, and TrecQA.
Figure 1: Overview of our Hybrid Co-Attention Network (HCAN). The model consists of three major components:(1) a hybrid encoder module that explores three types of encoders: deep, wide, and contextual; (2) a relevancematching module with external weights for learning soft term matching signals; (3) a semantic matching modulewith co-attention mechanisms for context-aware representation learning.
Table 2: Results on TrecQA, TwitterURL, and Quora.The best scores except for BERT are bolded
Table 4: Evaluation of different encoders in Sec. 2.1 (best numbers on each dataset are bolded).
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