Deep Learning for Answer Sentence Selection
Table 2: Results of our models (unigram and bigram) trained on both the TRAIN and TRAIN-ALL dataset. count indicates whether the overlapping word count features were also used.
['Model  [BOLD] TRAIN', 'Model  [BOLD] TRAIN', 'MAP', 'MRR']
As can be seen, the bigram model performs better than the unigram model and the addition of the IDF-weighted word count features significantly improve performance for both models by 10% – 15%. Wang et al. dataset TRAIN-ALL negatively impacted their models. This does not apply to our models, where performance of the models increases.