To capture fine-grained features, on the one hand, some works are concerned with matching QA pairs relationship in a more complex and diverse way, e.g., CNTN (Qiu and Huang, 2015) and MV-LSTM(Wan et al., 2016).
latent representation models aim to jointly learn lexical and semantic information from QA sentences and influence the vector generation directly, e.g., attention mechanism (Bahdanau et al., 2015).