Using Domain Knowledge to Guide Dialog Structure Induction via Neural
  Probabilistic Soft Logic
Connor Pryor Quan Yuan Jeremiah Liu Mehran Kazemi Deepak Ramachandran Tania Bedrax-Weiss Lise Getoor
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.