Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic
<|reference_start|>Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic: 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.<|reference_end|>
@article{pryor2024using,
    title={Using Domain Knowledge to Guide Dialog Structure Induction via Neural
  Probabilistic Soft Logic},
    author={Connor Pryor, Quan Yuan, Jeremiah Liu, Mehran Kazemi, Deepak
  Ramachandran, Tania Bedrax-Weiss, Lise Getoor},
    journal={arXiv preprint arXiv:2403.17853},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17853},
    primaryClass={cs.CL cs.LG}
}