Papers with domain semi-supervised setting
Semi-supervised Stochastic Multi-Domain Learning using Variational Inference (P19-1)
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| Challenge: | Supervised NLP models rely on large collections of text which closely resemble intended testing setting. however, data is often messy, with domain labels not always available, or providing limited information about the style and genre of text. |
| Approach: | They propose a method to distill the important domain signal as part of a multi-domain learning system using a latent variable model. |
| Outcome: | The proposed model improves performance over benchmark domain adaptation methods . text corpora are often collated from several different sources, including news, literature, microblogs, and web crawls . |