Semantic dependency parsing, which aims to find rich bi-lexical relationships, allows words to have multiple dependency heads, resulting in graph-structured representations. We propose an approach to semi-supervised learning of semantic dependency parsers based on the CRF autoencoder framework. Our encoder is a discriminative neural semantic dependency parser that predicts the latent parse graph of the input sentence. Our decoder is a generative neural model that reconstructs the input sentence conditioned on the latent parse graph. Our model is arc-factored and therefore parsing and learning are both tractable. Experiments show our model achieves significant and consistent improvement over the supervised baseline.
We propose an approach to semi-supervised learning of semantic dependency parsers based on the CRF autoencoder framework. This paper focuses on semi-supervised semantic dependency parsing using the CRF-autoencoder to train the model in a semi-supervised style, indicating effectiveness on low resource labeled data tasks.