Papers with cross-lingual relation classification

2 papers
The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification (2020.findings-emnlp)

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Challenge: Current approaches for relation classification are focused on the English language and require lots of training data with human annotations.
Approach: They propose a baseline model based on Multilingual BERT and a new multilingual pretraining setup . they propose 'relationship classification' models that use distant supervision .
Outcome: The proposed model significantly improves the baseline model with distant supervision.
Adversarial Feature Adaptation for Cross-lingual Relation Classification (C18-1)

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Challenge: Existing methods for relation classification exploit monolingual data due to lack of annotated data in other languages.
Approach: They propose an adversarial feature adaptation approach for cross-lingual relation classification using a generative adversarial network.
Outcome: The proposed approach yields an improvement of 5.7% over the state-of-the-art.

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