The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Adversarial Multi-task Learning for Text Classification
Open accessAbstract
Neural network models have shown their promising opportunities for multi-task learning, which focus on learning the shared layers to extract the common and task-invariant features.However, in most existing approaches, the extracted shared features are prone to be contaminated by task-specific features or the noise brought by other tasks.In this paper, we propose an adversarial multi-task learning framework, alleviating the shared and private latent feature spaces from interfering with each other.We conduct extensive experiments on 16 different text classification tasks, which demonstrates the benefits of our approach.Besides, we show that the shared knowledge learned by our proposed model can be regarded as off-the-shelf knowledge and easily transferred to new tasks.The datasets of all 16 tasks are publicly available at
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Sequence to Sequence Learning with Neural Networks
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A Survey on Multi-Task Learning
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“The non-redundancy is achieved by enforcing a soft orthogonality constraint between the two representations [5, 25, 47].”
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AdapterFusion: Non-Destructive Task Composition for Transfer Learning
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Metadata
- Authors
- Liu, P., Qiu, X., & Huang, X.
- Venue
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2017
- Pages
- 1-10
- OpenAlex
- W2962897020
- Cited by
- 601
- References
- 35
- FWCI
- 34.68
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Liu, P., Qiu, X., & Huang, X. (2017). Adversarial Multi-task Learning for Text Classification. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/p17-1001
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