19 February 2020, Computational Linguistics Seminar, Jonas Groschwitz

In this talk, I will discuss our parser for semantic graphs such as Abstract Meaning Representation (AMR). Our approach combines neural models with mechanisms from compositional semantic construction. Key to this approach is the Apply-Modify (AM) algebra, which we developed to both reflect linguistic principles and yield a simple parsing model. In particular, the AM algebra allows us to find consistent latent compositional structures for our training data, which is crucial when training a compositional parser. The parser then employs neural supertagging and dependency models to predict interpretable, meaningful operations that construct the semantic graph. The result is a semantic parser with strong performance across diverse graphbanks, that also provides insights to the compositional patterns of the graphs.