DLGNet-Task: An End-to-end Neural Network Framework for Modeling Multi-turn Multi-domain Task-Oriented Dialogue
Project Overview
The document explores the innovative application of generative AI in education through the introduction of DLGNet-Task, a cutting-edge end-to-end neural network framework specifically designed for task-oriented dialogue systems. This framework utilizes autoregressive transformer networks to enable effective multi-turn, multi-domain dialogues, offering enhanced controllability, explainability, and output verification. By addressing the inherent complexities and maintenance costs associated with traditional dialogue systems, DLGNet-Task achieves performance levels comparable to existing state-of-the-art models while simultaneously overcoming challenges related to the integration of open-domain and task-oriented dialogues. The findings suggest that such advancements in generative AI can significantly enhance educational tools by facilitating more natural and interactive communication between learners and AI systems, ultimately improving the learning experience and outcomes.
Key Applications
DLGNet-Task framework for task-oriented dialogue systems
Context: Multi-turn, multi-domain conversational AI system used in various applications such as booking services and IT help desks.
Implementation: The framework uses a modularized architecture that integrates components like natural language understanding, dialogue management, and natural language generation, trained jointly in an end-to-end manner.
Outcomes: Achieves controllable, explainable, and verifiable dialogue outputs while maintaining low development and maintenance costs.
Challenges: Performance is affected by the noise in training data and the complexity of integrating open-domain and task-oriented dialogues.
Implementation Barriers
Technical
Existing dialogue systems are often based on expensive rule-based heuristics and templates, making them difficult to scale.
Proposed Solutions: Adopting end-to-end neural architectures that streamline the dialogue generation process and reduce reliance on manual coding.
Data Quality
Errors in the training dataset can lead to reduced performance and noise in the model's outputs.
Proposed Solutions: The need for consistent and well-defined datasets to improve training and evaluation of dialogue systems.
Project Team
Oluwatobi O. Olabiyi
Researcher
Prarthana Bhattarai
Researcher
C. Bayan Bruss
Researcher
Zachary Kulis
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Oluwatobi O. Olabiyi, Prarthana Bhattarai, C. Bayan Bruss, Zachary Kulis
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai