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检索条件"任意字段=International Conference on Natural Language Processing and Knowledge Engineering"

共 26903 条记录，以下是281-290 订阅

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13th CCF international conference on natural language processing and Chinese Computing, NLPCC 2024

13th CCF International Conference on Natural Language Proces...

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13th CCF international conference on natural language processing and Chinese Computing, NLPCC 2024

ISBN: (纸本)9789819794423

The proceedings contain 194 papers. The special focus in this conference is on natural language processing and Chinese Computing. The topics include: Hierarchical knowledge Aggregation for Personalized Response Generation in Dialogue Systems;multi-hop Reading Comprehension Model Based on Abstract Meaning Representation and Multi-task Joint Learning;Leveraging Large language Models for QA Dialogue Dataset Construction and Analysis in Public Services;MCFC: A Momentum-Driven Clicked Feature Compressed Pre-trained language Model for Information Retrieval;integrating Syntax Tree and Graph Neural Network for Conversational Question Answering over Heterogeneous Sources;pqE: Zero-Shot Document Expansion for Dense Retrieval with Large language Models;CKF: Conditional knowledge Fusion Method for CommonSense Question Answering;MPPQA: Structure-Aware Extractive Multi-span Question Answering for Procedural Documents;GraphLLM: A General Framework for Multi-hop Question Answering over knowledge Graphs Using Large language Models;local or Global Optimization for Dialogue Discourse Parsing;structure and Behavior Dual-Graph Reasoning with Integrated Key-Clue Parsing for Multi-party Dialogue Reading Comprehension;enhancing Emotional Support Conversation with Cognitive Chain-of-Thought Reasoning;a Simple and Effective Span Interaction Modeling Method for Enhancing Multiple Span Question Answering;FacGPT: An Effective and Efficient Method for Evaluating knowledge-Based Visual Question Answering;PAPER: A Persona-Aware Chain-of-Thought Learning Framework for Personalized Dialogue Response Generation;towards Building a Robust knowledge Intensive Question Answering Model with Large language Models;model-Agnostic knowledge Distillation Between Heterogeneous Models;exploring Multimodal Information Fusion in Spoken Off-Topic Degree Assessment;integrating Hierarchical Key Information and Semantic Difference Features for Long Text Matching;CausalAPM: Generalizable Literal Disentanglement for NLU

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Evaluating and Adapting Large language Models to Represent Folktales in Low-Resource languages 4

Evaluating and Adapting Large Language Models to Represent F...

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4th international conference on natural language processing for Digital Humanities, NLP4DH 2024

作者： Meaney, J.A. Alex, Beatrice Lamb, William School of Languages Literatures and Cultures University of Edinburgh United Kingdom

ISBN: (纸本)9798891761810

Folktales are a rich resource of knowledge about the society and culture of a *** folklore research aims to use automated techniques to better understand these folktales, and it relies on abstract representations of the textual *** a number of large language models (LLMs) claim to be able to represent low-resource langauges such as Irish and Gaelic, we present two classification tasks to explore how useful these representations are, and three adaptations to improve the performance of these *** find that adapting the models to work with longer sequences, and continuing pre-training on the domain of folktales improves classification performance, although these findings are tempered by the impressive performance of a baseline SVM with non-contextual features. © 2024 Association for Computational Linguistics.

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Development of language Model on Biomedical Domain to Pretrain natural language processing

Development of Language Model on Biomedical Domain to Pretra...

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2024 international conference on Communication, Computer Sciences and engineering, IC3SE 2024

作者： Gunturu, Vijaya Priya, Yadavalli Devi Bachhav, Gayatri Vijayendra Praveena, K. Anandhi, R.J. Dhaliwal, Navdeep School Of Engineering Sr University Department Of Ece Telangana Warangal India Computer Science And Engineering Vignan's Institute Of Information Technology Vishakapatanam Andhra Pradesh Duvvada India Vppcoe Va Sion Mumbai Mumbai University India Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Information Science Engineering Bangalore India Lovely Professional University Phagwara India

ISBN: (纸本)9798350366846

Large neural language model like BERT can be pre trained to get extraordinary profits through multiple natural language processing task. Though, General Domain Corpora including web and news wire are focused on pre training efforts. The main specific pre training are benefited from general domain language models is considered as a prevailing assumption. The study focusses on the domain specific language model with abundance of unlabeled text like biomedical natural language processing and pre training from its scratch that results in more gains over the general domain language model. The investigation can be facilitated by compiling of biomedical NLP data sets that are publicly available. The experiment shows the pre training of domain specific model that act as a solid foundation in performing biomedical NLP task in wide range. the model is evaluated for modelling choices including task specific fine tuning and pre training. BERT models have some common practises involving named entity recognition using complex tagging schemes. The research can be accelerated with biomedical NLP for pre training and task specific model for the biomedical community and the leader board is created for biomedical language understanding and reasoning benchmark (BLURB). © 2024 IEEE.

关键词： natural language processing systems

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13th CCF international conference on natural language processing and Chinese Computing, NLPCC 2024

13th CCF International Conference on Natural Language Proces...

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13th CCF international conference on natural language processing and Chinese Computing, NLPCC 2024

ISBN: (纸本)9789819794331

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Federated Prototype Guided Adaption for Vision-language Models

Federated Prototype Guided Adaption for Vision-Language Mode...

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

作者： Liu, Youchao Huang, Dingjiang School of Data Science and Engineering East China Normal University Shanghai China

ISBN: (纸本)9798350368741

Federated Learning (FL) is a new pivotal paradigm for decentralized training on heterogeneous data. Recently fine-tuning of Vision-language Models (VLMs) has been extended to the federated setting to improve overall performance. Unfortunately, in this case, FL still faces two critical challenges that hinder its actual performance: data distribution heterogeneity and high resource costs brought by large VLMs. In this paper, we introduce FedPGA, a prototype-guided method, for achieving performance improvements in the federated setting for VLMs. Concretely, we design a prototype-based adapter for the vision-language model, CLIP. The lightweight adapter updates the prior knowledge encoded in CLIP to enhance its adaption capability further and avoid the effects of data distribution heterogeneity in the federated setting. Simultaneously, small-scale operations can mitigate the computational and communication burden caused by large VLMs. Our comprehensive empirical evaluations of nine diverse image classification datasets show that our method is superior to existing FL methods under VLMs. © 2025 IEEE.

关键词： federated learning few-shot classification prototype-based adapter vision-language model

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13th CCF international conference on natural language processing and Chinese Computing, NLPCC 2024

13th CCF International Conference on Natural Language Proces...

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13th CCF international conference on natural language processing and Chinese Computing, NLPCC 2024

ISBN: (纸本)9789819794300

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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...

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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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INTERPRETABLE VISUAL QUESTION ANSWERING REFERRING TO OUTSIDE knowledge 30

INTERPRETABLE VISUAL QUESTION ANSWERING REFERRING TO OUTSIDE...

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30th IEEE international conference on Image processing (ICIP)

作者： Zhu, He Togo, Ren Ogawa, Takahiro Haseyama, Miki Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido Japan Hokkaido Univ Fac Informat Sci & Technol Sapporo Hokkaido Japan

ISBN: (纸本)9781728198354

We present a novel multimodal interpretable VQA model that can answer the question more accurately and generate diverse explanations. Although researchers have proposed several methods that can generate human-readable and fine-grained natural language sentences to explain a model's decision, these methods have focused solely on the information in the image. Ideally, the model should refer to various information inside and outside the image to correctly generate explanations, just as we use background knowledge daily. The proposed method incorporates information from outside knowledge and multiple image captions to increase the diversity of information available to the model. The contribution of this paper is to construct an interpretable visual question answering model using multimodal inputs to improve the rationality of generated results. Experimental results show that our model can outperform state-of-the-art methods regarding answer accuracy and explanation rationality.

关键词： Visual question answering interpretable machine learning outside knowledge learning natural language explanations

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Enhancing natural language processing: A Comprehensive Review of Retrieval Augmented Generation 4

Enhancing Natural Language Processing: A Comprehensive Revie...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Shahade, Aniket K. Deshmukh, Priyanka V. Pune Campus Pune India

ISBN: (纸本)9798331540364

Retrieval augmented generation (RAG) has emerged as a promising approach in natural language processing, combining retrieval and generation techniques to produce high-quality text. By incorporating external knowledge into the generation process, RAG addresses the limitations of generative models that rely solely on learned information. This review explores the foundational principles and architecture of RAG, which typically involves a two-step process: retrieving relevant information based on the input query and generating text informed by both the query and the retrieved knowledge. The review discusses key considerations for the retriever and generator components, as well as the integration of retrieved knowledge into the generative model. By understanding the underlying principles and architecture of RAG, researchers and practitioners can leverage this powerful technique to improve the quality and contextual relevance of text generation tasks. © 2024 IEEE.

关键词： Nearest neighbor search

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13th CCF international conference on natural language processing and Chinese Computing, NLPCC 2024

13th CCF International Conference on Natural Language Proces...

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13th CCF international conference on natural language processing and Chinese Computing, NLPCC 2024

ISBN: (纸本)9789819794393

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