A Matrix-Vector Recurrent Unit Model for Capturing Compositional Semantics in Phrase Embeddings

This work presents a novel recurrent computational mechanism that specifically learns the compositionality by encoding the compositional rule of each word into a matrix and demonstrates that this model consistently outperforms the LSTM and CNN deep learning models, simple algebraic compositions, and other popular baselines on different datasets.