Papers with Cross-lingual text classification

2 papers
Cross-lingual Text Classification with Heterogeneous Graph Neural Network (2021.acl-short)

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Challenge: Existing methods for cross-lingual text classification only consider factors beyond semantic similarity, causing performance degradation between some language pairs.
Approach: They propose a method to incorporate heterogeneous information within and across languages for cross-lingual text classification using graph convolutional networks.
Outcome: The proposed method significantly outperforms state-of-the-art models on all tasks and achieves consistent performance gain over baselines in low-resource settings.
Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher (2020.findings-emnlp)

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Challenge: Existing approaches for transferring supervision across languages require expensive cross-lingual resources.
Approach: They propose a cross-lingual teacher-student method that generates "weak" supervision in a target language using minimal cross-linguistic resources.
Outcome: The proposed method outperforms state-of-the-art methods with a student classifier in 18 languages . it extracts and transfers only the most important task-specific seed words across languages based on translated seed words .

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