Low-Resource Semantic Role Labeling

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 1177–1187, Baltimore, Maryland, USA, June 23-25 2014. c⃝2014 Association for Computational Linguistics Low-Resource Semantic Role Labeling Matthew R. Gormley1 Margaret Mitchell2 Benjamin Van Durme1 Mark Dredze1 1Human Language Technology Center of Excellence Johns Hopkins University, Baltimore, MD 21211 2Microsoft Research Redmond, WA 98052 mrg@cs.jhu.edu | memitc@microsoft.com | vandurme@cs.jhu.edu | mdredze@cs.jhu.edu Abstract We explore the extent to which high- resource manual annotations such as tree- banks are necessary for the task of se- mantic role labeling (SRL). We examine how performance changes without syntac- tic supervision, comparing both joint and pipelined methods to induce latent syn- tax. This work highlights a new applica- tion of unsupervised grammar induction and demonstrates several approaches to SRL in the absence of supervised syntax. Our best models obtain competitive results in the high-resource setting and state-of- the-art results in the low resource setting, reaching 72.48% F1 averaged across lan- guages. We release our code for this work along with a larger toolkit for specifying arbitrary graphical structure.1 1 Introduction The goal of semantic role labeling (SRL) is to identify predicates and arguments and label their semantic contribution in a sentence. Such labeling defines who did what to whom, when, where and how. For example, in the sentence “The kids ran the marathon”, ran assigns a role to kids to denote that they are the runners; and a role to marathon to denote that it is the race course. Models for SRL have increasingly come to rely on an array of NLP tools (e.g., parsers, lem- matizers) in order to obtain state-of-the-art re- sults (Bj¨orkelund et al., 2009; Zhao et al., 2009). Each tool is typically trained on hand-annotated data, thus placing SRL at the end of a very high- resource NLP pipeline. However, richly annotated data such as that provided in parsing treebanks is expensive to produce, and may be tied to specific domains (e.g., newswire). Many languages do 1http://www.cs.jhu.edu/˜mrg/software/ not have such supervised resources (low-resource languages), which makes exploring SRL cross- linguistically difficult. The problem of SRL for low-resource lan- guages is an important one to solve, as solutions pave the way for a wide range of applications: Ac- curate identification of the semantic roles of enti- ties is a critical step for any application sensitive to semantics, from information retrieval to machine translation to question answering. In this work, we explore models that minimize the need for high-resource supervision. We ex- amine approaches in a joint setting where we marginalize over latent syntax to find the optimal semantic role assignment; and a pipeline setting where we first induce an unsupervised grammar. We find that the joint approach is a viable alterna- tive for making reasonable semantic role predic- tions, outperforming the pipeline models. These models can be effectively trained with access to only SRL annotations, and mark a state-of-the-art contribution for low-resource SRL. To better understand the effect of the low- resource grammars and features used in these models, we …

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