aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model
Table 7: Overview of previously published systems on the QA answer ranking task. All reported results are from the best setting of each model trained on TRAIN-ALL data.
['Method', 'MAP', 'MRR']
We can see aNMM trained with TRAIN-ALL set beats all the previous state-of-the art systems including both methods using feature engineering and deep learning models. These results are very promising since aNMM requires no manual feature engineering, no expensive processing by various NLP parsers and no external results like large scale knowledge base except for pre-trained word embeddings. Furthermore, even without combining additional features, aNMM still performs well for answer ranking, showing significant improvements over previous deep learning model with no additional features and linguistic feature engineering methods.