[Paper Review] A Unified Implicit Dialog Framework for Conversational Search

Song Feng, Chulaka Gunasekara+3 more|arXiv (Cornell University)|Feb 12, 2018

Speech and dialogue systems2 references3 citations

TL;DR

This paper proposes a unified, end-to-end trainable framework for conversational search that uses a centralized knowledge representation to implicitly derive dialog components like intent detection, state tracking, and query generation, enabling rapid adaptation across domains without hand-coded rules. The framework achieves 89% intent classification accuracy and supports plug-and-play semantic matching and feedback-driven improvement.

ABSTRACT

We propose a unified Implicit Dialog framework for goal-oriented, information seeking tasks of Conversational Search applications. It aims to enable dialog interactions with domain data without replying on explicitly encoded the rules but utilizing the underlying data representation to build the components required for dialog interaction, which we refer as Implicit Dialog in this work. The proposed framework consists of a pipeline of End-to-End trainable modules. A centralized knowledge representation is used to semantically ground multiple dialog modules. An associated set of tools are integrated with the framework to gather end users' input for continuous improvement of the system. The goal is to facilitate development of conversational systems by identifying the components and the data that can be adapted and reused across many end-user applications. We demonstrate our approach by creating conversational agents for several independent domains.

Motivation & Objective

• Address the challenge of high development cost and low reusability in goal-oriented conversational search systems due to hand-crafted rules and domain-specific logic.
• Enable seamless adaptation of conversational agents across diverse domains by decoupling dialog logic from explicit rule encoding.
• Facilitate continuous system improvement through integrated feedback collection from end users, domain experts, and annotators.
• Develop a modular, plug-and-play architecture that reuses core components across applications using a shared, domain-agnostic knowledge representation.
• Demonstrate the framework’s effectiveness in real-world domains such as apartment rental and restaurant search with minimal reconfiguration.

Proposed method

• Construct a Central Knowledge Representation (CKR) from domain knowledge bases, capturing entities, semantic relations, data types, and operations in a domain-agnostic manner.
• Use the CKR to semantically ground and initialize all dialog modules, including intent labeling, state tracking, and query generation.
• Implement a three-step semantic matching pipeline: literal matching, fuzzy matching, and vector representation matching using word embeddings.
• Train a natural language intent classifier on delexicalized utterances, achieving 89% accuracy on a 200+ utterance test set.
• Employ a dialog simulator in a Wizard-of-Oz setup to generate synthetic dialog data and collect real-time feedback from human agents.
• Integrate feedback into learning modules to enable continuous improvement of dialog components without retraining from scratch.

Experimental results

Research questions

• RQ1 Can a unified, domain-agnostic framework be built to support conversational search without relying on explicitly encoded dialog rules?
• RQ2 How effectively can a centralized knowledge representation enable the automatic derivation of dialog components like intent detection and state tracking?
• RQ3 To what extent can the same framework be reused across different domains with minimal reconfiguration?
• RQ4 Can real-time feedback from human agents improve dialog system performance in a continuous learning loop?
• RQ5 How does the performance of the implicit dialog framework compare to traditional rule-based or end-to-end approaches in terms of accuracy and adaptability?

Key findings

• The framework achieves 89% intent classification accuracy on a test set of over 200 utterances, demonstrating strong performance in intent detection without explicit rule engineering.
• The use of a centralized knowledge representation enables seamless adaptation of dialog components across different domains, such as apartment rental and restaurant search, with only database and CKR reconfiguration.
• The dialog simulator successfully captures real-time human-in-the-loop feedback, which is more informative than offline annotation and directly usable for model improvement.
• The modular, plug-and-play design allows for interchangeable semantic matching methods, supporting flexibility and extensibility in dialog system development.
• The framework reduces dependency on costly manual dialog logic design and annotated training data by reusing components grounded in the CKR.
• The system demonstrates comparable performance on new domains after CKR regeneration, indicating strong generalization and reusability across applications.