Paper: The Inside-Outside Recursive Neural Network model for Dependency Parsing

ACL ID D14-1081
Title The Inside-Outside Recursive Neural Network model for Dependency Parsing
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

We propose the first implementation of an infinite-order generative dependency model. The model is based on a new recursive neural network architecture, the Inside-Outside Recursive Neural Network. This architecture allows information to flow not only bottom-up, as in traditional recursive neural networks, but also top- down. This is achieved by computing content as well as context representations for any constituent, and letting these rep- resentations interact. Experimental re- sults on the English section of the Uni- versal Dependency Treebank show that the infinite-order model achieves a per- plexity seven times lower than the tradi- tional third-order model using counting, and tends to choose more accurate parses in k-best lists. In addition, reranking with this model achieves state-o...