aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model
Table 6: Results of TREC QA on TRAIN and TRAIN-ALL with combining additional features.
['Training Data Method', 'TRAIN MAP', 'TRAIN MRR', 'TRAIN-ALL MAP', 'TRAIN-ALL MRR']
[Yu:2014, Severyn:2015: LRS:2766462.2767738] and LSTM [DBLP:conf/acl/WangN15] for answer ranking when combining additional features. We combine the predicted score from aNMM-1 and aNMM-2 with the Query Likelihood (QL) [Croft:2009: SEI:1516224] score using LambdaMART [Wu:2010:ABI:1825381.1825396] following a similar approach to [DBLP:conf/acl/WangN15]. We compare the results with previous deep learning models with additional features. We can see that with combined features, both aNMM-1 and aNMM-2 have better performance. aNMM-1 also outperforms CNN by Severyn et al. [Severyn:2015:LRS:2766462.2767738] which is the current state-of-the-art method for ranking answers in terms of both MAP and MRR on TRAIN and TRAIN-ALL.