Dialogue Management
24 papers with code • 0 benchmarks • 1 datasets
( Image credit: Bocklisch et al. )
Benchmarks
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Latest papers with no code
Thread Detection and Response Generation using Transformers with Prompt Optimisation
To address these challenges an end-to-end model that identifies threads and prioritises their response generation based on the importance was developed, involving a systematic decomposition of the problem into discrete components - thread detection, prioritisation, and performance optimisation which was meticulously analysed and optimised.
Illuminate: A novel approach for depression detection with explainable analysis and proactive therapy using prompt engineering
This paper introduces a novel paradigm for depression detection and treatment using advanced Large Language Models (LLMs): Generative Pre-trained Transformer 4 (GPT-4), Llama 2 chat, and Gemini.
OmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System
Furthermore, to glean a nuanced understanding of OmniDialog's strengths and potential pitfalls, we designed a fine-grained analysis framework for dialogue-centric tasks.
Towards a Neural Era in Dialogue Management for Collaboration: A Literature Survey
Dialogue-based human-AI collaboration can revolutionize collaborative problem-solving, creative exploration, and social support.
Leveraging Large Language Models in Conversational Recommender Systems
A Conversational Recommender System (CRS) offers increased transparency and control to users by enabling them to engage with the system through a real-time multi-turn dialogue.
Multi-Task End-to-End Training Improves Conversational Recommendation
In this paper, we analyze the performance of a multitask end-to-end transformer model on the task of conversational recommendations, which aim to provide recommendations based on a user's explicit preferences expressed in dialogue.
Ericson: An Interactive Open-Domain Conversational Search Agent
Open-domain conversational search (ODCS) aims to provide valuable, up-to-date information, while maintaining natural conversations to help users refine and ultimately answer information needs.
Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management
By exploiting MoE-LM structure, our methods significantly reduce the size of the action space and improve the efficacy of RL-based DM.
Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning
Dialogue state tracking (DST) is an important step in dialogue management to keep track of users' beliefs.
A Survey on Conversational Search and Applications in Biomedicine
This paper aims to provide a radical rundown on Conversation Search (ConvSearch), an approach to enhance the information retrieval method where users engage in a dialogue for the information-seeking tasks.