Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
This work addresses semantic dependency parsing for natural language processing, offering an incremental improvement by incorporating second-order interactions into neural models.
The paper tackled semantic dependency parsing by proposing a second-order parser that considers interactions between pairs of edges, using mean field variational inference or loopy belief propagation approximated as neural network layers for end-to-end training, achieving state-of-the-art performance.
Semantic dependency parsing aims to identify semantic relationships between words in a sentence that form a graph. In this paper, we propose a second-order semantic dependency parser, which takes into consideration not only individual dependency edges but also interactions between pairs of edges. We show that second-order parsing can be approximated using mean field (MF) variational inference or loopy belief propagation (LBP). We can unfold both algorithms as recurrent layers of a neural network and therefore can train the parser in an end-to-end manner. Our experiments show that our approach achieves state-of-the-art performance.