Notably, another thread of SP-based approaches try to exploit IR-based techniques (Yao and Van Durme, 2014;Bast and Haussmann, 2015;Yang et al., 2014;Yih et al., 2015;Bao et al., 2016;Yavuz et al., 2016;Liang et al., 2016) by computing the similarity of two sequences as features, leveraging a neural network-based answer type prediction model, or training end-to-end neural symbolic machine via REINFORCE (Williams, 1992).
most SP-based approaches more or less rely on handcrafted rules or features, which limits their scalability and transferability.