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task-oriented dialogue

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DevOps
DevOps
May 29, 2024 · Artificial Intelligence

End-to-End Task-Oriented Dialogue Agent Construction Using Monte Carlo Simulation and LLM Fine-Tuning

This article presents an end‑to‑end approach for building task‑oriented dialogue agents by simulating user behavior with Monte Carlo methods, generating training data via LLMs, and efficiently fine‑tuning multiple large language models using LLaMA Factory, demonstrating significant improvements in intent recognition, slot filling, and contextual understanding.

Data GenerationLLM fine-tuningMonte Carlo simulation
0 likes · 17 min read
End-to-End Task-Oriented Dialogue Agent Construction Using Monte Carlo Simulation and LLM Fine-Tuning
Laiye Technology Team
Laiye Technology Team
Aug 29, 2022 · Artificial Intelligence

Evolution of Dialogue Management: From Rule‑Based to Data‑Driven Systems and Industrial Deployments

This article reviews the historical development of dialogue management—from early rule‑based and finite‑state approaches to modern data‑driven and reinforcement‑learning methods—and examines how major industry platforms such as Amazon Alexa, Amazon Lex, and RASA implement these techniques in practice.

Amazon AlexaDialogue ManagementNLU
0 likes · 16 min read
Evolution of Dialogue Management: From Rule‑Based to Data‑Driven Systems and Industrial Deployments
58 Tech
58 Tech
Jun 24, 2022 · Artificial Intelligence

Reinforcement Learning for Lead Generation in Task‑Oriented Dialogue Systems

This article presents a reinforcement‑learning‑based approach to improve lead‑capture efficiency of a task‑oriented chatbot used in local services, detailing the system architecture, RL algorithms (DQN/DDQN), data construction, model training, offline and online evaluation, and the resulting commercial gains.

ChatbotDQNcustomer service
0 likes · 27 min read
Reinforcement Learning for Lead Generation in Task‑Oriented Dialogue Systems
Airbnb Technology Team
Airbnb Technology Team
Nov 11, 2021 · Artificial Intelligence

Airbnb’s Task‑Oriented Dialogue System for Mutual Cancellation: Architecture, Data Collection, Modeling, and Deployment

Airbnb’s ATIS task‑oriented dialogue system for Mutual Cancellation combines hierarchical domain classification, Q&A‑style intent annotation, large‑scale RoBERTa pre‑training with multilingual fine‑tuning, multi‑turn context handling, GPU‑accelerated inference, and contextual‑bandit reinforcement learning to deliver a scalable, efficient customer‑support solution.

AICustomer SupportGPU deployment
0 likes · 22 min read
Airbnb’s Task‑Oriented Dialogue System for Mutual Cancellation: Architecture, Data Collection, Modeling, and Deployment
Laiye Technology Team
Laiye Technology Team
Jun 8, 2021 · Artificial Intelligence

Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue Systems

This paper presents a hierarchical reinforcement learning approach that jointly trains dialogue policy and natural language generation modules for task-oriented dialogue systems, achieving state‑of‑the‑art performance on MultiWOZ 2.0 and 2.1 while preserving response fluency.

MultiWOZNatural Language Generationdialogue policy
0 likes · 10 min read
Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue Systems
DataFunTalk
DataFunTalk
Dec 30, 2020 · Artificial Intelligence

Meta-Dialog System: Using Meta-Learning for Fast Adaptation and Robustness in Task-Oriented Conversational AI

This article presents a meta‑learning based end‑to‑end task‑oriented dialogue system that quickly adapts to new scenarios with limited data and improves robustness through a human‑machine collaboration decision module, validated on extended‑bAbI benchmarks and real‑world Alibaba Cloud customer‑service applications.

Human-Machine CollaborationMAMLdialogue system
0 likes · 15 min read
Meta-Dialog System: Using Meta-Learning for Fast Adaptation and Robustness in Task-Oriented Conversational AI
DataFunTalk
DataFunTalk
Nov 20, 2019 · Artificial Intelligence

Advances and Reflections on Human‑Machine Dialogue Technologies

This presentation reviews recent progress in spoken and multimodal dialogue systems, covering X‑driven architectures, task‑oriented and open‑domain approaches, NLU/DM integration, FAQ, KB/KG‑driven methods, document‑driven dialogue, and outlines remaining challenges and future research directions.

artificial intelligencedialogue systemsknowledge graph
0 likes · 21 min read
Advances and Reflections on Human‑Machine Dialogue Technologies
DataFunTalk
DataFunTalk
Oct 25, 2019 · Artificial Intelligence

Advances and Challenges in Human‑Machine Dialogue: Open‑Domain and Task‑Oriented Systems

This article reviews recent progress and open research problems in human‑machine dialogue, covering both open‑domain chat and task‑oriented systems, with focus on reply quality, decoding, retrieval‑augmented generation, controllable and personalized responses, multi‑turn modeling, reinforcement‑learning strategies, low‑resource NLU, and data augmentation techniques.

dialogue systemsnatural language processingopen-domain chat
0 likes · 16 min read
Advances and Challenges in Human‑Machine Dialogue: Open‑Domain and Task‑Oriented Systems