[Paper Review] Challenges in Building Intelligent Open-domain Dialog Systems

Minlie Huang, Xiaoyan Zhu+1 more|arXiv (Cornell University)|May 13, 2019

Topic Modeling167 references44 citations

TL;DR

This survey reviews neural approaches to open-domain dialog systems, focusing on semantics, consistency, and interactiveness, and discusses retrieval, generation, and hybrid methods to address these challenges.

ABSTRACT

There is a resurgent interest in developing intelligent open-domain dialog systems due to the availability of large amounts of conversational data and the recent progress on neural approaches to conversational AI. Unlike traditional task-oriented bots, an open-domain dialog system aims to establish long-term connections with users by satisfying the human need for communication, affection, and social belonging. This paper reviews the recent works on neural approaches that are devoted to addressing three challenges in developing such systems: semantics, consistency, and interactiveness. Semantics requires a dialog system to not only understand the content of the dialog but also identify user's social needs during the conversation. Consistency requires the system to demonstrate a consistent personality to win users trust and gain their long-term confidence. Interactiveness refers to the system's ability to generate interpersonal responses to achieve particular social goals such as entertainment, conforming, and task completion. The works we select to present here is based on our unique views and are by no means complete. Nevertheless, we hope that the discussion will inspire new research in developing more intelligent dialog systems.

Motivation & Objective

• Motivate the open-domain dialog goal of long-term user engagement and social interaction.
• Identify and articulate three core challenges: semantics, consistency, and interactiveness.
• Compare and synthesize neural approaches (retrieval, generation, hybrid) for open-domain dialogs.
• Discuss grounding in real-world knowledge and persona for more natural interactions.
• Outline evaluation methods and benchmarks to guide future research.

Proposed method

• Describe the end-to-end response generation framework for open-domain dialogs.
• Characterize retrieval-based, generation-based, and hybrid methods within a unified scoring formulation P( Y | X t, C t ).
• Explain shallow versus deep interaction networks for retrieval-based response ranking.
• Summarize encoder–decoder generation architectures, including the role of attention and pre-trained language models.
• Discuss grounding techniques such as knowledge grounding, persona grounding, and affect grounding.
• Survey evaluation approaches and common benchmarks for open-domain conversation modeling.

Experimental results

Research questions

• RQ1 What are the central semantic, consistency, and interactiveness challenges in neural open-domain dialog systems?
• RQ2 How do retrieval-based, generation-based, and hybrid architectures address these challenges?
• RQ3 What grounding strategies improve content richness and interpersonal quality of responses?
• RQ4 What evaluation methods and benchmarks best capture open-domain dialog quality and engagement?
• RQ5 How can future work advance long-term user engagement in open-domain conversations?

Key findings

• Neural open-domain dialogs often produce generic responses due to semantic issues, motivating methods to enrich understanding and grounding.
• Retrieval-based, generation-based, and hybrid approaches each have strengths and limitations, with hybrids providing a balance between realism and novelty.
• Deep interaction networks and Transformer-based models (e.g., BERT, GPT variants) improve matching and response quality in retrieval settings.
• Grounding in persona, knowledge, and emotion can enhance consistency and interactivity in conversations.
• Pre-trained language models and task-specific fine-tuning have driven strong results in recent conversational AI evaluations and challenges.