We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. To our knowledge, the conjunction of these formal properties is not supported by any existing parser. Our transition-based parser, which uses a novel transition set and features based on bidirectional LSTMs, has value not just for UCCA parsing: its ability to handle more general graph structures can inform the development of parsers for other semantic DAG structures, and in languages that frequently use discontinuous structures.
Recent interest in SDP has yielded numerous works on graph parsing @cite_64 @cite_5 @cite_9 @cite_47 , including tree approximation @cite_6 @cite_45 and joint syntactic semantic parsing @cite_62 @cite_71 .
@cite_64 @cite_62 @cite_9 @cite_6 @cite_45 @cite_71 @cite_5 @cite_47
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This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser. We use a larger but more thoroughly regularized parser than other recent BiLSTM-based approaches, with biaffine classifiers to predict arcs and labels. Our parser gets state of the art or near state of the art performance on standard treebanks for six different languages, achieving 95.7 UAS and 94.1 LAS on the most popular English PTB dataset. This makes it the highest-performing graph-based parser on this benchmark---outperforming Kiperwasser Goldberg (2016) by 1.8 and 2.2 ---and comparable to the highest performing transition-based parser (, 2016), which achieves 95.8 UAS and 94.6 LAS. We also show which hyperparameter choices had a significant effect on parsing accuracy, allowing us to achieve large gains over other graph-based approaches. In this shared task paper for SemEval2014 Task 8, we show that most semantic structures can be approximated by trees through a series of almost bijective graph transformations. We transform input graphs, apply off-the-shelf methods from syntactic parsing on the resulting trees, and retrieve output graphs. Using tree approximations, we obtain good results across three semantic formalisms, with a 15.9 error reduction over a stateof-the-art semantic role labeling system on development data. Our system came in 3 6 in the shared task closed track. This paper is a description of our system for SemEval-2015 Task 18: Broad-Coverage Semantic Dependency Parsing. We implement a hybrid parser which benefits from both transition-based and graph-based parsing approaches. In particular, the tree approximation method is explored to take advantage of wellstudied tree parsing techniques. Evaluation on multilingual data sets demonstrates that considerably good semantic analysis can be automatically built by applying state-of-the-art data-driven parsing techniques. We present the Potsdam systems that participated in the semantic dependency parsing shared task of SemEval 2014. They are based on linguistically motivated bidirectional transformations between graphs and trees and on utilization of syntactic dependency parsing. They were entered in both the closed track and the open track of the challenge, recording a peak average labeled F1 score of 78.60.