检索结果-内蒙古大学图书馆

Joint 30th International Conference on Computational Linguistics and 14th International Conference on language Resources and Evaluation, LREC-COLING 2024

作者： Subedi, Bipesh Regmi, Sunil Bal, Bal Krishna Acharya, Praveen Information and Language Processing Research Lab Department of Computer Science & Engineering Kathmandu University Nepal School of Computing Dublin City University Ireland

ISBN: (纸本)9782493814104

Large language Models (LLMs) have made significant advancements in Natural language processing (NLP) by excelling in various NLP tasks. This study specifically focuses on evaluating the performance of LLMs for Named Entity Recognition (NER) and Part-of-Speech (POS) tagging for a low-resource language, Nepali. The aim is to study the effectiveness of these models for languages with limited resources by conducting experiments involving various parameters and fine-tuning and evaluating two datasets namely, ILPRL and EBIQUITY. In this work, we have experimented with eight LLMs for Nepali NER and POS tagging. While some prior works utilized larger datasets than ours, our contribution lies in presenting a comprehensive analysis of multiple LLMs in a unified setting. The findings indicate that NepBERTa, trained solely in the Nepali language, demonstrated the highest performance with F1-scores of 0.76 and 0.90 in ILPRL dataset. Similarly, it achieved 0.79 and 0.97 in EBIQUITY dataset for NER and POS respectively. This study not only highlights the potential of LLMs in performing classification tasks for low-resource languages but also compares their performance with that of alternative approaches deployed for the tasks. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

Technological acceptance of non-native speakers on language learning mobile application using augmented reality

学校读者我要写书评

暂无评论

Multimedia Tools and Applications 2025年 1-32页

作者： Suwadi, Nur Asylah Lam, Meng Chun Majid, Nazatul Aini Abd Jalaluddin, Nor Hashimah Aisyah, Aznur Kasdan, Junaini Hussain, Afifuddin Husairi Ahmad, Azlan Ma’arof, Daing Zairi Mixed Reality and Pervasive Computing Lab Center for Artificial Intelligence Technology Faculty of Information Science & Technology Universiti Kebangsaan Malaysia Bangi43600 Malaysia Center for Research in Language and Linguistics Faculty of Social Sciences and Humanities Universiti Kebangsaan Malaysia Bangi43600 Malaysia Universiti Kebangsaan Malaysia Bangi43600 Malaysia Pusat Citra Universiti Universiti Kebangsaan Malaysia Bangi43600 Malaysia

Although mobile applications have been widely used, there is limited research on the acceptance of mobile-based applications for language learning among foreign learners. Hence, we propose an extended version of the Technology Acceptance Model (TAM) that analyzes the key aspects affecting the acceptance of the mobile application by foreign learners. The extended TAM includes external constructs such as augmented reality (AR), content quality, design quality, and interactivity to consider both system and individual levels. Eighty international students from various countries studying Malay were given a TAM questionnaire online. Results revealed positive effects, and notably, the external constructs of AR, content quality, and interactivity are prerequisite factors in extending the TAM. The findings revealed that interactivity is the most significant external construct of Perceived Ease of Use (PEOU), followed by Perceived Usefulness (PU), which exhibits a significant contribution to the user’s attitude. Therefore, these results could guide how educator and higher education centers should approach the incorporation of these technologies in classrooms. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Augmented reality

End States Guided Matching Network for Retrieval-based Multi-turn Conversation 3

学校读者我要写书评

暂无评论

End States Guided Matching Network for Retrieval-based Multi...

2021年第三届先进计算机技术、信息科学与通信国际会议(CTISC2021)

作者： Weixin Tan Dandan Song Yujin Gao Lab of High Volume language Information Processing & Cloud Computing Beijing Lab of Intelligent Information TechnologyBeijing Institute of Technology

ISBN: (纸本)9781665418683

Multi-turn conversation response selection aims to choose the best response from multiple candidates based on matching it with the dialogue context. Mostly, a response full of context-related information tends to be a proper ***, in some cases, a brief response like "ok" could be the more appropriate one. We find that it is a semantically ended conversation that a brief response usually comes after,so there is no need to provide any context-related information after that. Thus, in addition to match the response with context,it is also critical to recognize the state of whether a dialogue has ended, and learn how to get necessary information from context of different end states separately. To achieve this, we propose an end states guided matching network to determine and incorporate the end states by jointly consider the length of response and the local similarity between the response and last few utterances. In addition, we adopt multiple descriptive sequence representations for a more reliable matching *** results demonstrate that our model outperforms the state-of-the-art methods in multiple datasets.

关键词： information science Computational modeling Computer network reliability Reliability Data mining

Why Not Look One Step Ahead in Reinforcement Learning Based Knowledge Graph Reasoning?

学校读者我要写书评

暂无评论

SSRN

SSRN 2024年

作者： Wang, Hao Song, Dandan Wu, Zhijing Tian, YuHang Xu, Jing Laboratory of High-Volume Language Information Processing and Cloud Computing Beijing Lab of Intelligent Information Technology School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

Multi-hop reasoning is an effective and explainable method for query answering to improve interpretability by finding reasoning paths over knowledge graphs (KGs). Recent studies apply reinforcement learning-based (RL-based) methods with policy agents to solve this problem. However, they only focus on the sequential reasoning paths and the candidate nodes regardless of the local information of adjacency subgraphs and the semantic correlations between relations in every decision step. In this paper, we propose an RL-based multi-hop reasoning model, Look One Step Ahead (LOSA), which regards more expressive adjacency subgraphs rather than nodes as candidate actions and pays attention to the semantic correlations between relations. Specifically, an adjacency aggregation module encodes the information of subgraphs and transmits the encoding to the policy network to help make decisions, which can reduce backtracking and alleviate sparse-reward issues. Furthermore, a semantic matching module is designed to stress the semantic correlations of relations to improve the rationality of the reasoning paths. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed approach. © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Dong, Li Liu, Zhibin Jiang, Feibo Wang, Kezhi Changsha Social Laboratory of Artificial Intelligence Hunan University of Technology and Business Changsha China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China The Department of Computer and Information Sciences Northumbria University United Kingdom

Unmanned aerial vehicles (UAV) can be applied in many Internet of Things (IoT) systems, e.g., smart farms, as a data collection platform. However, the UAV-IoT wireless channels may be occasionally blocked by trees or high-rise buildings. Intelligent reflecting surface (IRS) can be applied to improve the wireless channel quality by smartly reflecting the signal via a large number of low-cost passive reflective elements. This paper aims to minimize the energy consumption of the system by jointly optimizing the deployment and trajectory of the UAV. The problem can be formulated as a mixed-integer- and-nonlinear-programming (MINLP), which is difficult to be addressed by the traditional solution, which may be easily fall into the local optimal. To address this issue, we propose a Joint Optimization framework of depLoyment and Trajectory (JOLT), where an adaptive whale optimization algorithm (AWOA) is applied to optimize the deployment of the UAV, and an elastic ring self-organizing map (ERSOM) is introduced to optimize the trajectory of the UAV. Specifically, in AWOA, a variable-length population strategy is applied to find the optimal number of stop points, and a nonlinear parameter a and a partial mutation rule are introduced to balance the exploration and exploitation. In ERSOM, a competitive neural network is also introduced to learn the trajectory of the UAV by competitive learning, and a ring structure is presented to avoid the trajectory intersection. Extensive experiments are carried out to show the effectiveness of the proposed JOLT framework. Copyright © 2022, The Authors. All rights reserved.

关键词： Trajectories

Oracle Bone Inscriptions Multi-modal Dataset

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Li, Bang Luo, Donghao Liang, Yujie Yang, Jing Ding, Zengmao Peng, Xu Jiang, Boyuan Han, Shengwei Sui, Dan Qin, Peichao Wu, Pian Wang, Chaoyang Qi, Yun Jin, Taisong Wang, Chengjie Huang, Xiaoming Shu, Zhan Ji, Rongrong Liu, Yongge Wu, Yunsheng Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education of China Anyang Normal University China Youtu Lab Tencent China Key Laboratory of Multimedia Trusted Perception and Effcient Computing Ministry of Education of China Xiamen University China Digital Culture Lab Tencent SSV Sustainable Social Value Dept University of Cambridge United Kingdom

Oracle bone inscriptions(OBI) is the earliest developed writing system in China, bearing invaluable written exemplifications of early Shang history and paleography. However, the task of deciphering OBI, in the current climate of the scholarship, can prove extremely challenging. Out of the 4,500 oracle bone characters excavated, only a third have been successfully identified. Therefore, leveraging the advantages of advanced AI technology to assist in the decipherment of OBI is a highly essential research topic. However, fully utilizing AI's capabilities in these matters is reliant on having a comprehensive and high-quality annotated OBI dataset at hand whereas most existing datasets are only annotated in just a single or a few dimensions, limiting the value of their potential application. For instance, the Oracle-MNIST dataset only offers 30k images classified into 10 categories. Therefore, this paper proposes an Oracle Bone Inscriptions Multi-modal Dataset(OBIMD), which includes annotation information for 10,077 pieces of oracle bones. Each piece has two modalities: pixel-level aligned rubbings and facsimiles. The dataset annotates the detection boxes, character categories, transcriptions, corresponding inscription groups, and reading sequences in the groups of each oracle bone character, providing a comprehensive and high-quality level of annotations. This dataset can be used for a variety of AI-related research tasks relevant to the field of OBI, such as OBI Character Detection and Recognition, Rubbing Denoising, Character Matching, Character Generation, Reading Sequence Prediction, Missing Characters Completion task and so on. We believe that the creation and publication of a dataset like this will help significantly advance the application of AI algorithms in the field of OBI research. Copyright © 2024, The Authors. All rights reserved.

关键词：

language-Agnostic Bias Detection in language Models with Bias Probing

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Koksal, Abdullatif Yalcin, Omer Faruk Akbiyik, Ahmet Kilavuz, M. Tahir Korhonen, Anna Schutze, Hinrich Center for Information and Language Processing LMU Munich Germany Munich Center for Machine Learning Germany Data Analytics and Computational Social Science University of Massachusetts Amherst United States Harvard Kennedy School United States Middle East Initiative Harvard Kennedy School United States Marmara University Turkey Language Technology Lab University of Cambridge United Kingdom

Pretrained language models (PLMs) are key components in NLP, but they contain strong social biases. Quantifying these biases is challenging because current methods focusing on fill-the-mask objectives are sensitive to slight changes in input. To address this, we propose a bias probing technique called labDet, for evaluating social bias in PLMs with a robust and language-agnostic method. For nationality as a case study, we show that labDet "surfaces" nationality bias by training a classifier on top of a frozen PLM on non-nationality sentiment detection. We find consistent patterns of nationality bias across monolingual PLMs in six languages that align with historical and political context. We also show for English BERT that bias surfaced by labDet correlates well with bias in the pretraining data;thus, our work is one of the few studies that directly links pretraining data to PLM behavior. Finally, we verify labDet's reliability and applicability to different templates and languages through an extensive set of robustness checks. We publicly share our code and dataset in https://***/akoksal/labDet. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Large Generative Model-assisted Talking-face Semantic Communication System

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Jiang, Feibo Tu, Siwei Dong, Li Pan, Cunhua Wang, Jiangzhou You, Xiaohu Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China School of Information Science and Engineering Hunan Normal University Changsha China Changsha Social Laboratoryof Artificial Intelligence Hunan University of Technology and Business Changsha China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The National Mobile Communications Research Laboratory Southeast University Nanjing China The Purple Mountain Laboratories Nanjing China

The rapid development of generative Artificial Intelligence (AI) continually unveils the potential of Semantic Communication (SemCom). However, current talking-face SemCom systems still encounter challenges such as low bandwidth utilization, semantic ambiguity, and diminished Quality of Experience (QoE). This study introduces a Large Generative Model-assisted Talking-face Semantic Communication (LGM-TSC) System tailored for the talking-face video communication. Firstly, we introduce a Generative Semantic Extractor (GSE) at the transmitter based on the FunASR model to convert semantically sparse talking-face videos into texts with high information density. Secondly, we establish a private Knowledge Base (KB) based on the Large language Model (LLM) for semantic disambiguation and correction, complemented by a joint knowledge base-semantic-channel coding scheme. Finally, at the receiver, we propose a Generative Semantic Reconstructor (GSR) that utilizes BERTVITS2 and SadTalker models to transform text back into a highQoE talking-face video matching the user’s timbre. Simulation results demonstrate the feasibility and effectiveness of the proposed LGM-TSC system. Copyright © 2024, The Authors. All rights reserved.

关键词： Latent semantic analysis

Look one step ahead through first-order aggregation in reinforcement learning-based knowledge graph reasoning

学校读者我要写书评

暂无评论

information Sciences 2025年 718卷

作者： Hao Wang Dandan Song Zhijing Wu YuHang Tian Jing Xu Laboratory of High-Volume Language Information Processing and Cloud Computing Beijing Lab of Intelligent Information Technology School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 Beijing China

Multi-hop reasoning is an effective and interpretable approach for query answering, as it finds reasoning paths over knowledge graphs (KGs) to enhance interpretability. Recent studies have applied reinforcement learning-based (RL-based) methods with policy agents to solve this problem. However, these methods primarily focus on sequential reasoning paths and candidate nodes, while overlooking the local structural information of adjacency subgraphs and the semantic correlations between relations at each decision step. In this paper, we propose a novel RL-based multi-hop reasoning model, L ook O ne S tep A head (LOSA), which leverages first-order aggregation to regard more expressive adjacency subgraphs rather than nodes as candidate actions and pays attention to the semantic correlations between relations. Specifically, an adjacency aggregation module encodes the local subgraph information and feeds the representations into the policy network to guide decision-making, thereby reducing backtracking and mitigating sparse-reward issues. Furthermore, a semantic matching module is designed to emphasize the semantic correlations of relations to improve the rationality of the reasoning paths. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed approach.

关键词：

Visual language Model based Cross-modal Semantic Communication Systems

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Jiang, Feibo Tang, Chuanguo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China School of Information Science and Engineering Hunan Normal University Changsha China Changsha Social Laboratory of Artificial Intelligence Hunan University of Technology and Business Changsha China The Department of Computer Science Brunel University London United Kingdom The School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

Semantic Communication (SC) has emerged as a novel communication paradigm in recent years, successfully transcending the Shannon physical capacity limits through innovative semantic transmission concepts. Nevertheless, extant Image Semantic Communication (ISC) systems face several challenges in dynamic environments, including low semantic density, catastrophic forgetting, and uncertain Signal-to-Noise Ratio (SNR). To address these challenges, we propose a novel Vision-language Model-based Cross-modal Semantic Communication (VLM-CSC) system. The VLM-CSC comprises three novel components: (1) Cross-modal Knowledge Base (CKB) is used to extract high-density textual semantics from the semantically sparse image at the transmitter and reconstruct the original image based on textual semantics at the receiver. The transmission of high-density semantics contributes to alleviating bandwidth pressure. (2) Memory-assisted Encoder and Decoder (MED) employ a hybrid long/short-term memory mechanism, enabling the semantic encoder and decoder to overcome catastrophic forgetting in dynamic environments when there is a drift in the distribution of semantic features. (3) Noise Attention Module (NAM) employs attention mechanisms to adaptively adjust the semantic coding and the channel coding based on SNR, ensuring the robustness of the CSC system. The experimental simulations validate the effectiveness, adaptability, and robustness of the CSC system. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics