Abstract
This article focuses on bilingual Semantic Role Labeling (SRL); its goal is to annotate semantic roles on both sides of the parallel bilingual texts (bi-texts). Since rich bilingual information is encoded, bilingual SRL has been applied in many natural-language processing (NLP) tasks such as machine translation (MT), cross-lingual information retrieval (IR), and the like. A feasible way of performing bilingual SRL is using monolingual SRL systems to perform SRL on each side of bi-texts separately. However, it is difficult to obtain consistent SRL results on both sides of bi-texts in this way. Some works have tried to jointly infer bilingual SRL because there are many complementary language cues on both sides of bi-texts and they reported better performance than monolingual systems. However, there are two limits in the existing methods. First, the existing methods often require high inference costs due to the complex objective function. Second, the existing methods fully adopt the candidates generated by monolingual SRL systems, but many candidates are discarded in the argument pruning or identification stage of monolingual systems. In this article, we propose two strategies to overcome these limits. We utilize a simple but efficient technique: Dual Decomposition to search for consistent results for both sides of bi-texts. On the other hand, we propose a method called Bi-Directional Projection (BDP) to recover arguments discarded in monolingual SRL systems.
We evaluate our method on a standard parallel benchmark: the OntoNotes dataset. The experimental results show that our method yields significant improvements over the state-of-the-art monolingual systems. In addition, our approach is also better and faster than existing methods due to BDP and Dual Decomposition.
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Index Terms
Bilingual Semantic Role Labeling Inference via Dual Decomposition
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