clclab @EMNLP

Phong presented our paper “The Inside-Outside Recursive Neural Network model for Dependency Parsing” at EMNLP in Qatar. Here is the abstract:

The Inside-Outside Recursive Neural Network model for Dependency Parsing

(published pdf here)

Phong Le & Willem Zuidema

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 topdown.
This is achieved by computing content as well as context representations for any constituent, and letting these representations interact. Experimental results on the English section of the Universal Dependency Treebank show that the infinite-order model achieves a perplexity
seven times lower than the traditional third-order model using counting, and tends to choose more accurate parses in k-best lists. In addition, reranking with this model achieves state-of-the-art unlabelled attachment scores and unlabelled exact match scores.