Multichannel CNN with Attention for Text Classification
Table 2: Results of our AMCNN models against other models. We use underline to highlight wins.
['Model', 'MR', 'Subj', 'MPQA', 'SST-1', 'SST-2']
We refer to our model as AMCNN-{1,3,5,7}. Here AMCNN-x stands for AMCNN with x channels which means there are x different representations of the text. As we can see, our model exceeds in 3 out of 5 tasks. For MR/Subj/MPQA, AMCNN-3 outperforms other baselines and we can get a rough observation that AMCNN-3 performs better than AMCNN-{5,7}, and they all perform better than AMCNN-1, which is a single channel model. This phenomenon indicates that multichannel representation is effective, but continuing to increase the size of the channels does not improve our model all the time. We conjecture that it would be better to choose x according to the number of informative words in the sentence. Take the following sentences for example: An undeniably gorgeous, terminally document of a troubadour, his acolytes, and the triumph of his band. Uplifting as only a document of the worst possibilities of mankind can be, and among the best films of the year.