Joint Learning Improves Semantic Role Labeling

Proceedings of the 43rd Annual Meeting of the ACL, pages 589–596, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Joint Learning Improves Semantic Role Labeling Kristina Toutanova Dept of Computer Science Stanford University Stanford, CA, 94305 kristina@cs.stanford.edu Aria Haghighi Dept of Computer Science Stanford University Stanford, CA, 94305 aria42@stanford.edu Christopher D. Manning Dept of Computer Science Stanford University Stanford, CA, 94305 manning@cs.stanford.edu Abstract Despite much recent progress on accu- rate semantic role labeling, previous work has largely used independent classifiers, possibly combined with separate label se- quence models via Viterbi decoding. This stands in stark contrast to the linguistic observation that a core argument frame is a joint structure, with strong dependen- cies between arguments. We show how to build a joint model of argument frames, incorporating novel features that model these interactions into discriminative log- linear models. This system achieves an error reduction of 22% on all arguments and 32% on core arguments over a state- of-the art independent classifier for gold- standard parse trees on PropBank. 1 Introduction The release of semantically annotated corpora such as FrameNet (Baker et al., 1998) and PropBank (Palmer et al., 2003) has made it possible to develop high-accuracy statistical models for automated se- mantic role labeling (Gildea and Jurafsky, 2002; Pradhan et al., 2004; Xue and Palmer, 2004). Such systems have identified several linguistically mo- tivated features for discriminating arguments and their labels (see Table 1). These features usually characterize aspects of individual arguments and the predicate. It is evident that the labels and the features of ar- guments are highly correlated. For example, there are hard constraints – that arguments cannot overlap with each other or the predicate, and also soft con- straints – for example, is it unlikely that a predicate will have two or more AGENT arguments, or that a predicate used in the active voice will have a THEME argument prior to an AGENT argument. Several sys- tems have incorporated such dependencies, for ex- ample, (Gildea and Jurafsky, 2002; Pradhan et al., 2004; Thompson et al., 2003) and several systems submitted in the CoNLL-2004 shared task (Carreras and M`arquez, 2004). However, we show that there are greater gains to be had by modeling joint infor- mation about a verb’s argument structure. We propose a discriminative log-linear joint model for semantic role labeling, which incorpo- rates more global features and achieves superior performance in comparison to state-of-the-art mod- els. To deal with the computational complexity of the task, we employ dynamic programming and re- ranking approaches. We present performance re- sults on the February 2004 version of PropBank on gold-standard parse trees as well as results on auto- matic parses generated by Charniak’s parser (Char- niak, 2000). 2 Semantic Role Labeling: Task Definition and Architectures Consider the pair of sentences, • [The GM-Jaguar pact]AGENT gives [the car market]RECIPIENT [a much-needed boost]THEME • [A much-needed boost]THEME was given to [the car market]RECIPIENT by [the GM-Jaguar pact]AGENT Despite the different syntactic positions of the la- beled phrases, we recognize that …

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