POLYGLOT: Multilingual Semantic Role Labeling with Unified Labels Alan Akbik Yunyao Li IBM Research Almaden Research Center 650 Harry Road, San Jose, CA 95120, USA fakbika,
[email protected] Abstract Semantic role labeling (SRL) identifies the predicate-argument structure in text with semantic labels. It plays a key role in un- derstanding natural language. In this pa- per, we present POLYGLOT, a multilingual semantic role labeling system capable of semantically parsing sentences in 9 differ- Figure 1: Example of POLYGLOT predicting English Prop- ent languages from 4 different language Bank labels for a simple German sentence: The verb “kaufen” is correctly identified to evoke the BUY.01 frame, while “ich” groups. The core of POLYGLOT are SRL (I) is recognized as the buyer,“ein neues Auto”(a new car) models for individual languages trained as the thing bought, and “dir”(for you) as the benefactive. with automatically generated Proposition Banks (Akbik et al., 2015). The key fea- Not surprisingly, enabling SRL for languages ture of the system is that it treats the other than English has received increasing atten- semantic labels of the English Proposi- tion. One relevant key effort is to create Proposi- tion Bank as “universal semantic labels”: tion Bank-style resources for different languages, Given a sentence in any of the supported such as Chinese (Xue and Palmer, 2005) and languages, POLYGLOT applies the corre- Hindi (Bhatt et al., 2009). However, the conven- sponding SRL and predicts English Prop- tional approach of manually generating such re- Bank frame and role annotation. The re- sources is costly in terms of time and experts re- sults are then visualized to facilitate the quired, hindering the expansion of SRL to new tar- understanding of multilingual SRL with get languages.