A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations
Table 5: Experimental results on SC.
['Model', 'P@1', 'MRR']
Since Hu et al. From the results, we can see that deep models gain larger improvements over BM25, compared with that on QA. This is because the mismatch problem is more serious on this dataset, and usually the first clause has few same keyword with the second one. The other results are mainly consistent with those on QA, and MV-LSTM still performs better than all baseline methods significantly, with 11.4% relative improvement on P@1 over the strongest baseline.