Learning to Paraphrase for Question Answering
Table 5: Model performance on WikiQA. +cnt: word matching features introduced in Yang et al. (2015). The base QA model is BiLSTM. Best results in each group are shown in bold.
['[BOLD] Method', '[BOLD] MAP', '[BOLD] MRR']
We report MAP and MMR which evaluate the relative ranks of correct answers among the candidate sentences for a question. Again, we observe that Para4QA outperforms related baselines (see BiLSTM, DataAugment, AvgPara, and SepPara). Ablation experiments show that performance drops most when NMT paraphrases are removed. When word matching features are used (see +cnt in the third block), Para4QA reaches state of the art performance.