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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic
%A Pryor, Connor
%A Yuan, Quan
%A Liu, Jeremiah
%A Kazemi, Mehran
%A Ramachandran, Deepak
%A Bedrax-Weiss, Tania
%A Getoor, Lise
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F pryor-etal-2023-using
%X 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.
%R 10.18653/v1/2023.acl-long.422
%U https://aclanthology.org/2023.acl-long.422
%U https://doi.org/10.18653/v1/2023.acl-long.422
%P 7631-7652

Markdown (Informal)

[Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic](https://aclanthology.org/2023.acl-long.422) (Pryor et al., ACL 2023)

• Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic (Pryor et al., ACL 2023)

ACL

• Connor Pryor, Quan Yuan, Jeremiah Liu, Mehran Kazemi, Deepak Ramachandran, Tania Bedrax-Weiss, and Lise Getoor. 2023. Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7631–7652, Toronto, Canada. Association for Computational Linguistics.