Combining word embeddings and convolutional neural networks to detect duplicated questions
Table 1: Performance comparison of different models on the Quora dataset
['Model', 'Val. Accuracy', 'Test Accuracy']
Each model’s validation accuracy (if published) and test accuracy are listed in the columns. The models are sorted by decreasing test accuracy. Note that all models with ♠ were reported by Wang et al. in their work [WHF17]. Our baselines are denoted in bold in the last four rows of the table. Each baseline is labeled by the embedding approach used during training. Our experimental results surpass some models in terms of test accuracy and are also competitive with the state-of-the-art models. Among our models, we found Word2Vec to be the most consistent embedding approach.