Semi-supervised Question Retrieval with Gated Convolutions
Table 5: Comparision between model variants on the test set when question bodies are used or not used.
['TF-IDF', 'MAP', 'MRR', 'P@1']
TF-IDF’s performance changes very little when the question bodies are included (MRR and P@1 are slightly better but MAP is slightly worse). However, we find that the inclusion of the question bodies improves the performance of the RCNN model, achieving a 1% to 3% improvement with both model variations. The RCNN model’s greater improvement illustrates the ability of the model to pick out components that pertain most directly to the question being asked from the long, descriptive question bodies.