Neural Relation Prediction for Simple Question Answering over Knowledge Graph
Table 2: The impact of different text matching channels on the proposed instance-based Q′−Q network
['Model', 'Accuracy (%)']
In the next step, we evaluate the performance of our model while considering exact lexical match as a separate channel in the our Q′−Q network. To this end, three experiments were done and compared: (1) single-channel semantic text matching (Cosine function), (2) single-channel lexical match (indicator function), and (3) two-channel lexical and semantic text matching. As can be seen in the results, the impact of adding an extra input channel is obvious as it is compared with single one. The better performance of the semantic channel shows the importance of semantic text matching in the QA system. The further improvement using an additional channel for lexical match indicates that although lexical match is considered implicitly in the normal MatchPyramid model, it is not enough for considering this issue in the QA task and the proposed two-channel model can better cover both semantic and lexical similarities.