Web: http://arxiv.org/abs/2209.06638

Sept. 15, 2022, 1:14 a.m. | Wanwei He, Yinpei Dai, Binyuan Hui, Min Yang, Zheng Cao, Jianbo Dong, Fei Huang, Luo Si, Yongbin Li

cs.CL updates on arXiv.org arxiv.org

Pre-training methods with contrastive learning objectives have shown
remarkable success in dialog understanding tasks. However, current contrastive
learning solely considers the self-augmented dialog samples as positive samples
and treats all other dialog samples as negative ones, which enforces dissimilar
representations even for dialogs that are semantically related. In this paper,
we propose SPACE-2, a tree-structured pre-trained conversation model, which
learns dialog representations from limited labeled dialogs and large-scale
unlabeled dialog corpora via semi-supervised contrastive pre-training.
Concretely, we first define a …

arxiv pre-training semi-supervised space training tree understanding

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