The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment prediction, six-way question classification and Twitter sentiment prediction by distant supervision. The network achieves excellent performance in the first three tasks and a greater than 25% error reduction in the last task with respect to the strongest baseline.
Kalchbrenner et al. REF put forward Dynamic Convolutional Neural Network (DCNN) to model sentences of varying length, which used dynamic k-max pooling to explicitly capture short and long-range relations without relying on a parse tree.
A Convolutional Neural Network for Modelling Sentences
ArXiv
ArXiv
Computer Science