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Emotional knowledge Self-Distillation in Dialogue

Emotional Knowledge Self-Distillation in Dialogue

引用

2025 ieee international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Jian, Zhongquan Wang, Yancheng Wu, Weichao Yao, Junfeng Wang, Meihong Wu, Qingqiang School of Informatics Xiamen University Xiamen China School of Film Xiamen University Xiamen China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia School of Mechanics and Civil Engineering China University of Mining & Technology Beijing China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Xiamen China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen China

ISBN: (纸本)9798350368741

Recognizing emotions in dialogues is vital for effective human-computer interaction, yet remains a challenging task in natural language processing (NLP). Previous studies in Emotion Recognition in Conversation (ERC) have primarily focused on contextual features, while overlooking the importance of emotional features in emotion recognition. To address this gap, we focus on the role of emotional features in ERC and propose a novel method, Emotional knowledge Self-Distillation (EmoKSD1), to enhance the model's emotional sensitivity. In EmoKSD, utterances are enriched with implicit 〈mask〉 tokens to represent conveyed emotions, allowing the distillation of emotional knowledge from explicit emotional tokens to implicit 〈mask〉 tokens, thereby enhancing the model's ability to perceive subtle emotions within the dialogue. Through thorough evaluations on two public ERC datasets (i.e., IEMOCAP and MELD) using proposed coarse-grained utterance distillation and fine-grained token distillation techniques, EmoKSD demonstrates superior performance compared to existing methods, highlighting the significance of emotional features in ERC. © 2025 ieee.

关键词： Dialogue System Emotion Recognition in Conversation knowledge Distillation natural language processing

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Exploring Artificial Intelligence to Assist Kinematics Analysis of Rigid Bodies 10

Exploring Artificial Intelligence to Assist Kinematics Analy...

引用

10th international conference on engineering and Emerging Technologies, ICEET 2024

作者： Morano-Okuno, Hector Rafael Sandoval-Benitez, Guillermo School of Engineering and Science Tecnologico de Monterrey Mexico

ISBN: (纸本)9798331532895

Artificial Intelligence applications have spread to various areas of knowledge. Some of the applications of Machine Learning are Large language Models and natural language processing. An efficient way of communication between users and Artificial Intelligence information models is through Prompt engineering. This article explores using Artificial Intelligence to assist in the kinematics analysis of rigid bodies through Prompt engineering. The results obtained from the kinematic analysis of a Large language Model are evaluated through a case study. Among the findings, it is determined that the results of the analysis depend on the clarity and content of details provided by the user in the creation of the Prompts used to make the queries, as well as the user's expertise in the subject of kinematic analysis of rigid bodies since it must be determined the certainty of the answers provided by Artificial Intelligence. © 2024 ieee.

关键词： engineering education

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Advancing Capability Matching in Manufacturing Reconfiguration with Large language Models 1

引用

33rd international conference on Flexible Automation and Intelligent Manufacturing

作者： Mo, Fan Chaplin, Jack C. Sanderson, David Ratchev, Svetan Univ Nottingham Inst Adv Mfg Nottingham NG8 England Univ Nottingham Ctr Aerosp Mfg Nottingham NG7 2GX Notts England

ISBN: (数字)9783031744853

ISBN: (纸本)9783031744846;9783031744853

This paper introduces an approach that integrates natural language processing (NLP) and knowledge graphs with Reconfigurable Manufacturing Systems (RMS) to enhance flexibility and adaptability. We utilize a chatbot interface powered by GPT-4 and a structured knowledge base to simplify the complexities of manufacturing reconfiguration. This system not only boosts reconfiguration efficiency but also broadens accessibility to advanced manufacturing technologies. We demonstrate our methodology through an application in capability matching, showcasing how it facilitates the identification of assets for new product requirements. Our results indicate that this integrated solution offers a scalable and user-friendly approach to overcoming adaptability challenges in modern manufacturing environments.

关键词： natural language processing chatbot large language model manufacturing reconfiguration knowledge graph

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Can LLaMA 3 Understand Monetary Policy? 17

Can LLaMA 3 Understand Monetary Policy?

引用

17th international conference on Advanced Computer Theory and engineering

作者： Wu, Yingyi Xiao, Zhaomin Zhang, Jinran Mai, Zhelu Xu, Zhuoer Meta Menlo Pk CA 94025 USA Univ North Texas Denton TX 76203 USA I 66 Express Mobil Partners Manassas VA USA Hewlett Packard Enterprise Houston TX USA

ISBN: (纸本)9798331539894;9798331539887

This study provides an in-depth examination of LLaMA 3's performance on a domain-specific task, specifically classifying monetary policy texts. The aim is to categorize these texts as hawkish, dovish, or neutral by using a prompt to assess LLaMA 3's understanding of finance-related knowledge anticipated to be acquired during its pretraining. Experimental results demonstrate that LLaMA 3 has indeed developed the necessary commonsense knowledge to interpret monetary policy, as it surpasses both major and random baselines. To further understand its performance, we also analyze the consistency of its impressive results across different model inputs, including the number of tokens, nouns, and verbs.

关键词： natural language processing large language models finance monetary policy

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PRO-HAN: A HETEROGENEOUS GRAPH ATTENTION NETWORK FOR PROFILE-BASED SPOKEN language UNDERSTANDING 49

PRO-HAN: A HETEROGENEOUS GRAPH ATTENTION NETWORK FOR PROFILE...

引用

49th ieee international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Teng, Dechuan Lu, Chunlin Xu, Xiao Che, Wanxiang Qin, Libo Harbin Inst Technol Res Ctr Social Comp & Informat Retrieval Harbin Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Recently, Profile-based Spoken language Understanding (SLU) has gained increasing attention, which aims to incorporate various types of supplementary profile information (i.e., knowledge Graph, User Profile, Context Awareness) to eliminate the prevalent ambiguities in user utterances. However, existing approaches can only separately model different profile information, without considering their interrelationships or excluding irrelevant and conflicting information within them. To address the above issues, we introduce a Heterogeneous Graph Attention Network to perform reasoning across multiple PROfile information, called PRO-HAN. Specifically, we design three types of edges, denoted as intra-PRO, inter-PRO, and utterance-PRO, to capture interrelationships among multiple PROs. We establish a new state-of-the-art on the ProSLU dataset, with an improvement of approximately 8% across all three metrics. Further analysis experiments also confirm the effectiveness of our method in modeling multi-source profile information.

关键词： Heterogeneous Graph Neural Networks Spoken language Understanding knowledge Graph User Profile Context Awareness

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AI Assisted Domain Modeling Explainability and Traceability 24

AI Assisted Domain Modeling Explainability and Traceability

引用

ACM/ieee 27th international conference on Model Driven engineering languages and Systems: Companion Proceedings (MODELS)

作者： Silva, Jonathan Univ Luxembourg Belval Esch Sur Alzett Luxembourg

ISBN: (纸本)9798400706226

Domain Models are abstract representations of selected elements in a domain that is created in a collaborative process between domain and modeler experts. The participants share domain knowledge to conceptualize and reason about the elements that will create the domain models. Through this exchange, a comprehensive and accurate representation of the domain is achieved, ensuring that the model captures the relevant aspects and relationships in the domain. Research in Artificial Intelligence (AI) has explored various methods to assist in the creation of domain models from text using natural language processing (NLP) and Machine Learning (ML). Recent advancements with Large language Models (LLMs) have shown that it is possible to create domain models using prompting techniques;however, the generated domain models contain errors and remain constrained by the performance of the LLM used. Despite the impressive capabilities of LLMs to create domain models, it is evident that it does not address the needs of domain and modelers experts that participate in the creation of domain models. Every AI technique has its advantages and limitations that must be integrated with human feedback in a collaboration process. Therefore, we propose an approach that incorporates human-AI collaboration supported by AI assistants that follows a dialogue approach to understand the users needs and purpose to suggest relevant models. Our proposal combines symbolic and subsymbolic AI techniques with explainability and traceability of the decisions that assist to create domain models that are relevant for the users.

关键词： Domain Modeling Large language Models Uncertainty Explainability Traceability

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KG-CQAM: knowledge Graph and Mind Mapping-based Complex Question Answering for Large language Models 5

KG-CQAM: Knowledge Graph and Mind Mapping-based Complex Ques...

引用

5th international conference on Control, Robotics, and Intelligent System, CCRIS 2024

作者： Lai, Xuan Tang, Lianggui Zhu, Xiuling Xiao, Liyong Chen, Zhuo Yang, Jiajun The Artificial Intelligence College of Chongqing Technology and Business University Chongqing China Chongqing Key Laboratory of IntelliSense and Blockchain Technology Chongqing China

ISBN: (数字)9781510685871

ISBN: (纸本)9781510685864

Large language Models (LLMs) have achieved significant advancements in the field of natural language processing. However, they still face challenges related to integrity, timeliness, fidelity, and adaptability, often encountering hallucination issues. To address these challenges, knowledge graphs, as structured data structures storing vast knowledge in a clear manner, can be employed to augment LLMs’ knowledge. However, simple retrieval methods are insufficient for complex multi-hop reasoning tasks. Therefore, this paper proposes a novel approach, utilizing the chain of thought, employing mind maps as key prompts, aimed at dynamically reasoning over knowledge graphs by invoking LLMs while preserving graph structure information. Experimental results demonstrate that KG-CQAM not only significantly enhances LLMs’ reasoning capabilities for multi-hop complex questions but also obviates the need for any model training. © 2024 SPIE.

关键词： knowledge graph

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Assistance for Visually Impaired individuals using Artificial Intelligence and natural language processing

Assistance for Visually Impaired individuals using Artificia...

引用

2025 ieee international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Sathvig, Sri K. Bharathi.ln, Priyanka Santhosh.m Ramya, J. St. Joseph's College of Engineering Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331529833

People with Visual Impairments are vulnerable to various challenges in their everyday lives ranging from Access to Information, Travelling to places, Recognizing their surroundings, Isolated Lifestyle and much more. The Proposed solution aims to address these socially inclusive problems through the use of Artificial Intelligence, natural language processing, Machine Learning and Computer Vision. The feature index involves using Object Detection models like Yolo v5 for Obstacle avoidance, Integration of Google Map API for Seamless Navigation and NLP based Optical Character Recognition (OCR) for Text Recognition. An average accuracy of 86 percentage was achieved across all models. This solution aims to provide Better Lifestyle, Increased accessibility and Reduced Dependability for the Community of Visually Impaired Individuals. © 2025 ieee.

关键词： Artificial Intelligence Computer Vision Machine Learning natural language processing

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KnowLog: knowledge Enhanced Pre-trained language Model for Log Understanding 24

KnowLog: Knowledge Enhanced Pre-trained Language Model for L...

引用

46th ieee/ACM international conference on Software engineering, ICSE 2024

作者： Ma, Lipeng Jiang, Sihang Yang, Weidong Fei, Ben Xu, Bo Liang, Jiaqing Zhou, Mingjie Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China School of Computer Science and Technology Donghua University Shanghai China School of Data Science Fudan University Shanghai China

ISBN: (纸本)9798400702174

Logs as semi-structured text are rich in semantic information, making their comprehensive understanding crucial for automated log analysis. With the recent success of pre-trained language models in natural language processing, many studies have leveraged these models to understand logs. Despite their successes, existing pre-trained language models still suffer from three weaknesses. Firstly, these models fail to understand domain-specific terminology, especially abbreviations. Secondly, these models struggle to adequately capture the complete log context information. Thirdly, these models have difficulty in obtaining universal representations of different styles of the same logs. To address these challenges, we introduce KnowLog, a knowledge-enhanced pre-trained language model for log understanding. Specifically, to solve the previous two challenges, we exploit abbreviations and natural language descriptions of logs from public documentation as local and global knowledge, respectively, and leverage this knowledge by designing novel pre-training tasks for enhancing the model. To solve the last challenge, we design a contrastive learning-based pre-training task to obtain universal representations. We evaluate KnowLog by fine-tuning it on six different log understanding tasks. Extensive experiments demonstrate that KnowLog significantly enhances log understanding and achieves state-of-the-art results compared to existing pre-trained language models without knowledge enhancement. Moreover, we conduct additional experiments in transfer learning and low-resource scenarios, showcasing the substantial advantages of KnowLog. Our source code and detailed experimental data are available at https://***/LeaperOvO/KnowLog. © 2024 ieee Computer Society. All rights reserved.

关键词： Computational linguistics

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Constructing a Medical Domain Functional knowledge Graph with Large language Models 6

Constructing a Medical Domain Functional Knowledge Graph wit...

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6th ieee international conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Ji, Lintian Du, Shuxin Qiu, Yi Xu, Hongyi Guo, Xiaorui School of Engineering Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Huzhou China School of Engineering Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems Huzhou China

ISBN: (纸本)9798350374315

The integration of medicine and artificial intelligence is creating novel opportunities to gather, analyze, and generate groundbreaking medical insights from the vast expanse of medical literature. Although these advancements hold great promise, difficulties including manual annotation and precise data retrieval, and transparency persist. Advanced natural language processing (NLP) techniques and large language models (LLMs) have shown potential in addressing these issues. This paper introduces the Medical knowledge Graph (MedKG), a comprehensive and multidisciplinary medical knowledge graph. By utilizing advanced NLP algorithms, MedKG extracts millions of entities and converts them into triples from a corpus of superior medical research papers published over the last decade. It categorizes unstructured medical information into nine distinct tags, including disease names, treatments, diagnoses, symptoms, genetics, cellular structures of diseases, patient data, medical procedures, and treatment applications, while seamlessly integrating the digital object identifier (DOI) of each paper. As the latest structured database of medical knowledge, MedKG serves as a powerful tool for accelerating medical research and lays the groundwork for constructing a more extensive medical knowledge graph using full-text medical articles. Moreover, our research establishes a foundation for practical knowledge management systems based on text mining, applicable not only to complex medical systems but also to other professional domains. © 2024 ieee.

关键词： knowledge graph

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