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Summary: This study found that in a French corpus of clinical cases, the FlauBERT language model paired with a simple linear network architecture performed the best for intent and slot prediction by a chatbot.
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Summary: This paper proposes a scalable medical knowledge-assisted mechanism (MKA) to help general neural generative models perform better in medical conversation tasks. The mechanism includes a medical-specific knowledge graph with various medical-related information and a defined token concatenation policy. Evaluation results show that models combined with the MKA mechanism outperform original methods in multiple automatic evaluation metrics, with MKA-BERT-GPT achieving state-of-the-art performance.
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Summary: In this work, a multi-goal multi-agent learning framework is proposed for task-oriented dialogue generation, which includes KB-oriented and context-oriented teacher agents, as well as a student agent. A two-to-one teacher-student learning method coordinates these three networks to help the student integrate expert knowledge and achieve comprehensive performance. Additionally, dialogue memory network and KB memory network are employed to filter and extract information dynamically, improving system effectiveness.
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Qingbin Liu et al.
Summary: End-to-end task-oriented dialogue systems face challenges in effectively incorporating external knowledge bases and addressing entity imbalance. The proposed HRGNN-AO method utilizes heterogeneous relational graphs, shared-private parameterization, and hierarchical attention mechanism to improve response generation, while introducing an adaptive objective to handle entity imbalance and outperform state-of-the-art dialogue systems.
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Article
Computer Science, Artificial Intelligence
Xiaozhi Wang et al.
Summary: The paper introduces a unified model for Knowledge Embedding and Pre-trained Language Representation (KEPLER), which integrates entity descriptions into pre-trained language models to optimize knowledge embedding and language modeling objectives, achieving state-of-the-art performances in NLP tasks and KG link prediction.
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Proceedings Paper
Computer Science, Information Systems
Dongdong Li et al.
Summary: The study aims to achieve medical dialogue generation through an end-to-end variational reasoning approach, introducing patient states and physician actions as latent variables for explicit tracking and learning. Experimental results demonstrate the superior performance of the proposed method in medical dialogue generation, and the semi-supervised reasoning method shows comparable performance to fully supervised methods in physician policy learning.
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O Bodenreider
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