Low-Hanging Fruit: Knowledge Distillation from Noisy Teachers for Open Domain Spoken Language Understanding

Author:

Chen Cheng,Xing Bowen,Tsang Ivor W.

Publisher

Springer Nature Switzerland

Reference49 articles.

1. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence);A Aghaebrahimian,2016

2. Aghaebrahimian, A., Jurcícek, F.: Open-domain factoid question answering via knowledge graph search. In: Proceedings of the Workshop on Human-Computer Question Answering, pp. 22–28 (2016)

3. Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877–1901 (2020)

4. Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

5. Chen, C., Lyu, Y., Tsang, I.W.: Adversary-aware partial label learning with label distillation. arXiv preprint arXiv:2304.00498 (2023)

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