Max Pooling

We propose a novel neural network model for machine reading, DER Network, which ex- plicitly implements a reader building dynamic meaning representations for entities by gath- ering and accumulating information …...

AbstractRecurrent Neural Network (RNN) is one of the most popular architectures used in Natural Language Processsing (NLP) tasks because its recurrent structure is very suitable to process variable-length text. RNN …...

AbstractWe introduce a novel mixed characterword architecture to improve Chinese sentence representations, by utilizing rich semantic information of word internal structures. Our architecture uses two key strategies. The first is …...

AbstractPooling is an important technique for learning text representations in many neural NLP models. In conventional pooling methods such as average, max and attentive pooling, text representations are weighted summations …...

AbstractPooling-based recurrent neural architectures consistently outperform their counterparts without pooling on sequence classification tasks. However, the reasons for their enhanced performance are largely unexamined. In this work, we examine three …...

How to combine several pieces of evidence to verify a claim is an interesting semantic task. Very complex methods have been proposed, combining different evidence vectors using an evidence interaction …...