Papers with VRNN
Learning Interpretable Latent Dialogue Actions With Less Supervision (2022.aacl-main)
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| Challenge: | supervised neural dialogue modeling requires a significant amount of work to obtain turn-level labels, usually with dialogue state annotation. |
| Approach: | They propose a novel architecture for explainable modeling of task-oriented dialogues with discrete latent variables to represent dialogue actions. |
| Outcome: | The proposed model outperforms previous approaches with less supervision in terms of perplexity and BLEU on three datasets. |
Structured Attention for Unsupervised Dialogue Structure Induction (2020.emnlp-main)
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| Challenge: | Using structured attention, a model can learn dialogue structure in unsupervised fashion. |
| Approach: | They propose to incorporate structured attention layers into a Variational Recurrent Neural Network model with discrete latent states to learn dialogue structure in an unsupervised fashion. |
| Outcome: | The proposed model learns semantic structures similar to templates used to generate a dialogue corpus on two-party datasets and on multi-party dialogues, disentangling dialogues without human annotation. |