Adaptive Semantic Compositionality for Sentence Modelling

Representing a sentence with a fixed vector has shown its effectiveness in various NLP tasks. Most of the existing methods are based on neural network, which recursively apply different composition functions to a sequence of word vectors thereby obtaining a sentence vector. A hypothesis behind these approaches is that the meaning of any phrase can be composed of the meanings of its constituents. However, many phrases, such as idioms, are apparently non-compositional. To address this problem, we introduce a parameterized compositional switch, which outputs a scalar to adaptively determine whether the meaning of a phrase should be composed of its two constituents. We evaluate our model on five datasets of sentiment classification and demonstrate its efficacy with qualitative and quantitative experimental analysis.