Cross-lingual Transfer of Semantic Role Labeling Models
Mikhail Kozhevnikov,Ivan Titov +1 more
- pp 1190-1200
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TLDR
This work addresses the problem of transferring an SRL model from one language to another using a shared feature representation and assesses competitive performance as compared to a state-of-the-art unsupervised SRL system and a cross-lingual annotation projection baseline.Abstract:
Semantic Role Labeling (SRL) has become one of the standard tasks of natural language processing and proven useful as a source of information for a number of other applications. We address the problem of transferring an SRL model from one language to another using a shared feature representation. This approach is then evaluated on three language pairs, demonstrating competitive performance as compared to a state-of-the-art unsupervised SRL system and a cross-lingual annotation projection baseline. We also consider the contribution of different aspects of the feature representation to the performance of the model and discuss practical applicability of this method. 1 Background and Motivationread more
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Metaphor Detection with Cross-Lingual Model Transfer
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TL;DR: This paper presents a two-stage method to enable the construction of SRL models for resourcepoor languages by exploiting monolingual SRL and multilingual parallel data and shows that this method outperforms existing methods.
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Cross-lingual structure transfer for relation and event extraction
TL;DR: It is found that language-universal symbolic and distributional representations are complementary for cross-lingual structure transfer, and the event argument role labeling model transferred from English to Chinese achieves similar performance as the model trained from Chinese.
References
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