Papers by Daniel Hsu
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 . |
Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training (D19-1)
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| Challenge: | Current weakly supervised approaches for learning aspect classifiers require many fine-grained aspect labels, which are labor-intensive to obtain. |
| Approach: | They propose a weakly supervised approach that leverages seed words for aspect detection . they propose supervised student-teacher approach that uses teacher to train student models . |
| Outcome: | The proposed approach outperforms previous weakly supervised approaches by 14.1 F1 points on average in six domains of product reviews and six multilingual datasets of restaurant reviews. |
Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health (D19-55)
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| Challenge: | Existing weakly supervised learning frameworks are used for segment classification . lack of segment labels prevents the use of standard supervised methods . |
| Approach: | They propose a model that uses weak supervision to train supervised models for segment-level classification . they propose sigmoid attention mechanism-based aggregation function to improve the model . |
| Outcome: | The proposed model outperforms state-of-the-art models for segment-level sentiment classification by 9.8% in F1 . |