论文标题
先进的条件变异自动编码器（A-CVAE）：通过解开潜在特征表示来解释开放域的对话
Advanced Conditional Variational Autoencoders (A-CVAE): Towards interpreting open-domain conversation generation via disentangling latent feature representation
论文作者
论文摘要
目前，基于端到端深度学习的开放域对话系统仍然是黑匣子模型，使其易于与数据驱动的模型生成不相关的内容。具体而言，由于缺乏先验知识来指导训练，潜在变量在潜在空间中与不同的语义纠缠在一起。为了解决这个问题，本文提议通过涉及介质量表特征分离的认知方法来利用先验知识来利用生成模型。特别是，该模型将宏观级别的指导类别知识和微级开放域对话数据集成为培训，并将先验知识利用到潜在空间中，从而使模型能够将潜在变量置于介质范围内的潜在变量。此外，我们为开放域对话提出了一个新的指标，该指标可以客观地评估潜在空间分布的解释性。最后，我们在不同的数据集上验证了我们的模型，并在实验上证明我们的模型能够比其他模型产生更高的质量和更容易解释的对话。
Currently end-to-end deep learning based open-domain dialogue systems remain black box models, making it easy to generate irrelevant contents with data-driven models. Specifically, latent variables are highly entangled with different semantics in the latent space due to the lack of priori knowledge to guide the training. To address this problem, this paper proposes to harness the generative model with a priori knowledge through a cognitive approach involving mesoscopic scale feature disentanglement. Particularly, the model integrates the macro-level guided-category knowledge and micro-level open-domain dialogue data for the training, leveraging the priori knowledge into the latent space, which enables the model to disentangle the latent variables within the mesoscopic scale. Besides, we propose a new metric for open-domain dialogues, which can objectively evaluate the interpretability of the latent space distribution. Finally, we validate our model on different datasets and experimentally demonstrate that our model is able to generate higher quality and more interpretable dialogues than other models.
