Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs
Table 3: Comparison on the CoNLL 2009 English test set. The first block presents results of other models evaluated for both syntax and semantics on the CoNLL 2009 task. The second block presents our models. The third block presents the best published models, each using its own syntactic preprocessing.
['[BOLD] Model', '[BOLD] LAS', '[BOLD] Sem.  [ITALIC] F1  [BOLD] (WSJ)', '[BOLD] Sem.\xa0 [ITALIC] F1  [BOLD] (Brown)', '[BOLD] Macro  [ITALIC] F1']
English All of our models (Syntax-only, Semantics-only, Hybrid and Joint) improve over \newciteGesmundo:09 and \newciteHenderson:13, demonstrating the benefit of our entire-parser-state representation learner compared to the more locally scoped model. \newciteLei:15 use syntactic parses to obtain interaction features between predicates and their arguments and then compress feature representations using a low-rank tensor. \newciteTackstrom:15 present an exact inference algorithm for SRL based on dynamic programming and their local and structured models make use of many syntactic features from a pipeline; our search procedure is greedy. Their algorithm is adopted by \newciteFitzgerald:15 for inference in a model that jointly learns representations from a combination of PropBank and FrameNet annotations; we have not experimented with extra annotations.