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Learning Interpretable Latent Dialogue Actions With Less Supervision. (arXiv:2209.11128v2 [cs.CL] UPDATED)
Sept. 26, 2022, 1:15 a.m. | Vojtěch Hudeček, Ondřej Dušek
cs.CL updates on arXiv.org arxiv.org
We present a novel architecture for explainable modeling of task-oriented
dialogues with discrete latent variables to represent dialogue actions. Our
model is based on variational recurrent neural networks (VRNN) and requires no
explicit annotation of semantic information. Unlike previous works, our
approach models the system and user turns separately and performs database
query modeling, which makes the model applicable to task-oriented dialogues
while producing easily interpretable action latent variables. We show that our
model outperforms previous approaches with less supervision …
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