Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic (2403.17853v1)

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.

• The paper introduces an innovative method that integrates domain knowledge into dialog structure induction via Neural Probabilistic Soft Logic to boost accuracy.
• The methodology encodes domain-specific soft constraints into an iterative training process, reducing data requirements while enhancing learning efficiency.
• The approach demonstrates generalizability across diverse dialog domains, paving the way for more human-like and adaptable AI communication systems.

Leveraging Domain Knowledge for Dialog Structure Induction with Neural Probabilistic Soft Logic

Introduction

The paper presents a novel approach that utilizes domain knowledge to enhance dialog structure induction through Neural Probabilistic Soft Logic (NPSL). The authors propose a method that seamlessly integrates explicit domain knowledge into the training of neural models for dialog structure induction. This integration aims to guide and constrain the learning process, thereby improving the model's ability to infer dialog structures accurately.

Background and Significance

Dialog structure induction is a task in NLP that involves identifying and organizing the underlying structure and components within conversations. This task is crucial for understanding and generating human-like dialogs in AI applications. Traditional methods heavily rely on large datasets and deep learning models, which often ignore the wealth of available domain-specific knowledge that could enhance performance. The introduction of NPSL in this context represents a significant shift towards leveraging such domain knowledge to inform and constrain the neural model's training process.

Methodology

The proposed method incorporates domain knowledge into the dialog structure induction process in the form of soft constraints within the NPSL framework. These constraints represent prior knowledge about the dialog structure and guide the model toward more plausible structure predictions. The key components of the methodology include:

• Neural Probabilistic Soft Logic (NPSL): A flexible framework that allows for the integration of soft logic constraints with probabilistic reasoning in neural networks.
• Domain Knowledge Representation: The method by which domain-specific knowledge is encoded as soft constraints. These constraints are expressed in a way that the neural model can utilize during the learning process.
• Induction Process: An iterative training process where the NPSL model, informed by domain knowledge constraints, learns to infer dialog structures from the data.

Evaluation

The evaluation of the presented method focuses on its ability to accurately induce dialog structures across several benchmark datasets. The key findings from the evaluation include:

• Enhanced Performance: The method demonstrates superior performance in dialog structure induction tasks compared to baseline models that do not utilize domain knowledge.
• Efficiency in Learning: Incorporation of domain knowledge allows for more efficient learning, requiring fewer data samples to achieve comparable levels of accuracy.
• Generalizability: The approach shows promising results in generalizing across different dialog domains, indicating its adaptability to various types of dialogues.

Implications and Future Directions

The incorporation of domain knowledge into dialog structure induction models as proposed in this paper has significant theoretical and practical implications:

• Improved Understanding of Dialog Structures: The method provides a deeper understanding of dialogues by leveraging explicit knowledge about dialog components and their interrelations.
• Enhanced AI Communication Systems: By improving the accuracy of dialog structure induction, AI communication systems can become more nuanced and human-like in their interactions.
• Future Research Directions: This work opens up new avenues for research, particularly in exploring the integration of domain knowledge in other areas of NLP and AI. Future work could also focus on expanding the types of domain knowledge and constraints that can be incorporated into the NPSL framework.

In summary, this paper offers a significant contribution to the field of dialog structure induction by introducing a method that leverages domain knowledge to guide and enhance the learning process within the NPSL framework. The findings underscore the importance of incorporating domain-specific knowledge in improving the performance and efficiency of neural models in dialog structure induction tasks. This work not only advances our understanding of dialog structure induction but also sets the stage for future developments in creating more sophisticated and human-like AI communication systems.