Friday, October 13, 2017
12:30 PM
Wegmans Hall 2506
Xiaochang Peng
University of Rochester
Mapping natural language sentences to semantic graphs
As statistical natural language processing systems have progressed to provide deeper representations, there has been renewed interest in graph-based representations of semantic structures, and in algorithms to produce them. In this talk, we present three different approaches that take as input a sentence, and produce a semantic graph representation of the sentence as the output. We make an analogy of semantic parsing to the task of phrase-based machine translation and present a synchronous-graph-grammar based approach that uses a hyperedge replacement grammar (HRG) to analyze the semantic graph side. We further describe a neural sequence-to-sequence model which treats the target semantic graph representation as the output sequence. As a third approach, we propose a cache transition system that generalizes standard transition-based dependency parsing techniques to generate a graph rather than a tree, where a cache with fixed size m is included to analyze different classes of semantic graphs. We evaluate these approaches on a semantic graph representation called Abstract Meaning Representation (AMR) and empirical results show that these approaches achieve competitive results in comparison with other systems.
This talk will be live-streamed.