aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model
Table 5: Results of TREC QA on TRAIN-ALL without combining additional features (Compare with methods using feature engineering).
['Method', 'MAP', 'MRR']
We find that both aNMM-1 and aNMM-2 achieve better performance comparing with other methods using feature engineering. Specifically, comparing the results of aNMM-1 with the strongest baseline by Yih et al. [yih-EtAl:2013:ACL2013] based on enhanced lexical semantic models, aNMM-1 achieves 4.13% gain for MAP and 3.83% gain for MRR. These results show that it is possible to build a uniform deep learning model such that it can achieve better performance than methods using feature engineering. To the best of our knowledge, aNMM is the first deep learning model that can achieve good performance comparing with previous methods either based on deep learning models or feature engineering for ranking answers without any additional features, syntactic parsers and external resources except for pre-trained word embeddings.