Convolutional Neural Network Architectures for Matching Natural Language Sentences
Table 1: Sentence Completion.
['Model', 'P@1(%)']
The two proposed models get nearly half of the cases Arc-II outperforms Arc-I significantly, showing the power of joint modeling of matching and sentence meaning. As another convolutional model, SENNA+MLP performs fairly well on this task, although still running behind the proposed convolutional architectures since it is too shallow to adequately model the sentence. It is a bit surprising that uRAE comes last on this task, which might be caused by the facts that 1) the representation model (including word-embedding) is not trained on Reuters, and 2) the split-sentence setting hurts the parsing, which is vital to the quality of learned sentence representation.