Dialog Structure Induction (DSI) is the task of inferring the latent dialog structure (i.e., a set of dialog states and their temporal transitions) of a given goal-oriented dialog. It is a critical component for modern dialog system design and discourse analysis. Existing DSI approaches are often purely data-driven, deploy models that infer latent states without access to domain knowledge, underperform when the training corpus is limited/noisy, or have difficulty when test dialogs exhibit distributional shifts from the training domain. This work explores a neural-symbolic approach as a potential solution to these problems. We introduce Neural Probabilistic Soft Logic Dialogue Structure Induction (NEUPSL DSI), a principled approach that injects symbolic knowledge into the latent space of a generative neural model. We conduct a thorough empirical investigation on the effect of NEUPSL DSI learning on hidden representation quality, few-shot learning, and out-of-domain generalization performance. Over three dialog structure induction datasets and across unsupervised and semi-supervised settings for standard and cross-domain generalization, the injection of symbolic knowledge using NEUPSL DSI provides a consistent boost in performance over the canonical baselines.

中文翻译：

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使用领域知识通过神经概率软逻辑指导对话结构归纳

对话结构归纳 (DSI) 是推断给定目标导向对话的潜在对话结构（即一组对话状态及其时间转换）的任务。它是现代对话系统设计和话语分析的关键组件。现有的 DSI 方法通常是纯粹数据驱动的，部署的模型在无法访问领域知识的情况下推断潜在状态，在训练语料库有限/嘈杂时表现不佳，或者在测试对话表现出与训练域的分布变化时遇到困难。这项工作探索了神经符号方法作为这些问题的潜在解决方案。我们引入神经概率软逻辑对话结构归纳（NEUPSL DSI），这是一种将符号知识注入生成神经模型的潜在空间的原理方法。我们对 NEUPSL DSI 学习对隐藏表示质量、小样本学习和域外泛化性能的影响进行了彻底的实证研究。在三个对话结构归纳数据集以及针对标准和跨域泛化的无监督和半监督设置中，使用 NEUPSL DSI 注入符号知识可在规范基线上提供一致的性能提升。