Convolutional Neural Network Architectures for Matching Natural Language Sentences
Table 2: Tweet Matching.
['Model', 'P@1(%)']
This task is slightly easier than Experiment I , with more training instances and purely random negatives. It requires less about the grammatical rigor but more on detailed modeling of loose and local matching patterns (e.g., work-overtime⇔ rest). Again Arc-II beats other models with large margins, while two convolutional sentence models Arc-I and SENNA+MLP come next.