检索结果-内蒙古大学图书馆

Exploring the Potential of Large language Models (LLMs) for Low-resource languages: A Study on Named-Entity Recognition (NER) and Part-Of-Speech (POS) Tagging for Nepali language 30

Exploring the Potential of Large Language Models (LLMs) for ...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Machine translation of Chinese long sentences based on recognition of implicit period and comma 23

引用

23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Zhang, Wenjuan Li, Manjia Feng, Wenhe Lab of language engineering and computing Center for Linguistics and Applied Linguistics Guangdong University of Foreign Studies China

ISBN: (纸本)9780000000002

The translation of long sentences has always been a difficult problem for machine translation. In this paper, based on the feature that a considerable number of commas (intra-sentence punctuation) and periods (inter-sentence punctuation) in Chinese text can be transformed into each other, we propose the concepts of "implicit period" (comma that can be transformed into period) and "implicit comma" (period that can be transformed into comma), and realize their automatic recognition to transform Chinese long sentences into short sentences for Chinese-English machine translation. In this paper, a dataset of implicit period and comma is constructed by combining manual and semi-supervised learning methods, and an implicit period and comma recognition method is realized based on a pre-trained model, in which Hierarchical BERT, which has the best performance, is used as the subsequent application model. In this paper, a Chinese-English machine translation method based on implicit period and comma recognition is realized. The experiments based on pre-trained machine translation models on the WMT2018 (News) and WMT2023 (Literature) corpus show that for the English translation of long Chinese sentences, the method in this paper improves the BLEU value compared to the benchmark translation as a whole, and the effect of the method in this paper is more obvious the longer the sentence is for the relatively robust machine translation model. © 2024 China National Conference on Computational Linguistics.

关键词： Machine translation

来源：评论

学校读者我要写书评

暂无评论

Socially Sustainable HRI for Underrepresented Communities: Case Study of language Learning of Arab Children in Israel 25

Socially Sustainable HRI for Underrepresented Communities: C...

引用

ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Einat Gonen Amna Abu-Mukh Goren Gordon Department of Hebrew language and Semitic languages Tel-Aviv University Tel-Aviv Israel Curiosity Lab Department of Industrial Engineering Tel-Aviv University Tel-Aviv Israel Department of Informatics Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN USA

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

Social robots have predominantly been deployed in resource-rich environments and managed by technical research teams. We propose a socially sustainable framework for deploying social robots in underrepresented, under-resourced communities. This approach emphasizes population-specific social impact, affordable robotic platforms, and “programming“ and operation by local community members. We present a holistic and sociallyecological case study of a unique population: Arabic elementary school children in Israel who face specific linguistic challenges. Learning Hebrew as a second language is crucial for their future, but teaching resources are limited. We developed a unique curriculum designed to teach bilingual morphological awareness in a fun and engaging way, implementing it through social robotic platforms. These platforms were selected based on social sustainability criteria, including affordability, lack of required programming skills by educators, and ease of deployment in schools. In two long-term, in-the-wild studies conducted at two different elementary schools (N=135, N=87), we utilized first Patricc and then Valera, both open-source, 3D-printed robots. These robots were “programmed“ by a non-technical educator and operated by local teachers. After six activities conducted over several weeks, our results show significant positive learning gains in both Arabic and Hebrew morphological awareness. We discuss the implications of our socially sustainable framework for using social robots in underrepresented and under-resourced communities.

关键词： Social robots Education Linguistics Sustainable development Programming profession Faces

来源：评论

学校读者我要写书评

暂无评论

Socially Sustainable HRI for Underrepresented Communities: Case Study of language Learning of Arab Children in Israel 20

Socially Sustainable HRI for Underrepresented Communities: C...

引用

20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Gonen, Einat Abu-Mukh, Amna Gordon, Goren Tel-Aviv University Department of Hebrew language and Semitic languages Tel-Aviv Israel Tel-Aviv University Curiosity Lab Department of Industrial Engineering Tel-Aviv Israel Luddy School of Informatics Computing and Engineering Indiana University Department of Informatics BloomingtonIN United States

ISBN: (纸本)9798350378931

Social robots have predominantly been deployed in resource-rich environments and managed by technical research teams. We propose a socially sustainable framework for deploying social robots in underrepresented, under-resourced communities. This approach emphasizes population-specific social impact, affordable robotic platforms, and 'programming' and operation by local community members. We present a holistic and sociallyecological case study of a unique population: Arabic elementary school children in Israel who face specific linguistic challenges. Learning Hebrew as a second language is crucial for their future, but teaching resources are limited. We developed a unique curriculum designed to teach bilingual morphological awareness in a fun and engaging way, implementing it through social robotic platforms. These platforms were selected based on social sustainability criteria, including affordability, lack of required programming skills by educators, and ease of deployment in schools. In two long-term, in-the-wild studies conducted at two different elementary schools (N=135, N=87), we utilized first Patricc and then Valera, both open-source, 3D-printed robots. These robots were 'programmed' by a non-technical educator and operated by local teachers. After six activities conducted over several weeks, our results show significant positive learning gains in both Arabic and Hebrew morphological awareness. We discuss the implications of our socially sustainable framework for using social robots in underrepresented and under-resourced communities. © 2025 IEEE.

关键词： Teaching

来源：评论

学校读者我要写书评

暂无评论

Test-Time Training-Free Domain Adaptation 48

Test-Time Training-Free Domain Adaptation

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Feng, Yongxiang He, Weihua You, Kaichao Liu, Bing Zhang, Ziyang Wang, Yaoyuan Li, Minglei Lou, Yihang Li, Jiawei Li, Guoqi Liao, Jianxing Huawei Technologies Co. Ltd. Advanced Computing and Storage Lab China Peking University School of Electronics Engineering and Computer Science China Huawei Technologies Co. Ltd. Language & Speech Innovation Lab China Huawei Technologies Co. Ltd. GoTen AI Lab Department of Intelligent Vision China Huawei Technologies Co. Ltd. Department of Production Automation Development China Chinese Academy of Sciences Institute of Automation China

ISBN: (纸本)9781728163277

Deploying deep learning models to new environments is very challenging. Domain adaptation (DA) is a promising paradigm to solve the problem by collecting and adapting to unlabeled data in new environments. Though research efforts have led to steady performance improvement over the past decade, DA algorithms are still hard to deploy, as training on unlabeled new data makes tuning difficult and not feasible for inference-only devices. To make DA practical, in this paper we study a new problem named Test-time Training-Free Domain Adaptation (TTDA), where trained models must adapt to a single input (mimicking the test-time scenario) without training. By exploiting spatial activation that was previously overlooked and simply averaged out, we propose a simple method based on Feature Statistics Transformation (FST) on-the-fly for each test example. The proposed algorithm is tested in the TTDA setting on two standard DA benchmarks. Surprisingly, it surpasses or performs on par with state-of-the-art DA methods, even though they require additional training. We envision that this training-free paradigm has the potential to bring DA to embedded devices and would be of interest to audience of community. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Meeting the Growing Needs in Scientific and Technological Terms with China’s Terminology Management Agency – CNCTST 4th

Meeting the Growing Needs in Scientific and Technological Te...

引用

4th International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET – AI 2021

作者： Du, Jiali Alexandris, Christina Pei, Yajun Lian, Yuming Yu, Pingfang Lab of Language Engineering and Computing Faculty of Chinese Language and Culture Guangdong University of Foreign Studies Guangzhou China National University of Athens Athens Greece China National Committee for Terms in Sciences and Technologies Beijing China Beijing International Institute of Urban Development Beijing China

ISBN: (纸本)9783030732707

China National Committee for Terms in Sciences and Technologies (CNCTST) is a top management agency in China. It was established in 1985, aiming to introduce new terminology, endorse changes of borrowed terminology, alter the standards of terminology, explain unfamiliar terminology, and adopt practice to make terminology more accurate. CNCTST tries to standardize terminology practices in the country. Under the management of CNCTST, a considerable number of terminology databases have been constructed to serve the academic research and practical translation. Furthermore, CNCTST officially launched the first phase of DISE (data intelligent search engine) platform named TERMONLINE on June 15, 2016, and launched the revised second phase of TERMONLINE on September 17, 2020. This article will discuss both different periods of CNCTST and its successful management in Chinese terminology research and practices. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Terminology

来源：评论

学校读者我要写书评

暂无评论

A General Framework to Evaluate Methods for Assessing Dimensions of Lexical Semantic Change Using LLM-Generated Synthetic Data

arXiv

引用

arXiv 2025年

作者： Baes, Naomi Merx, Raphaël Haslam, Nick Vylomova, Ekaterina Dubossarsky, Haim Melbourne School of Psychological Sciences The University of Melbourne Australia School of Computing and Information Systems The University of Melbourne Australia School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom The Alan Turing Institute London United Kingdom Language Technology Lab University of Cambridge United Kingdom

Lexical Semantic Change (LSC) offers insights into cultural and social dynamics. Yet, the validity of methods for measuring kinds of LSC has yet to be established due to the absence of historical benchmark datasets. To address this gap, we develop a novel three-stage evaluation framework that involves: 1) creating a scalable, domain-general methodology for generating synthetic datasets that simulate theory-driven LSC across time, leveraging In-Context Learning and a lexical database;2) using these datasets to evaluate the effectiveness of various methods;and 3) assessing their suitability for specific dimensions and domains. We apply this framework to simulate changes across key dimensions of LSC (SIB: Sentiment, Intensity, and Breadth) using examples from psychology, and evaluate the sensitivity of selected methods to detect these artificially induced changes. Our findings support the utility of the synthetic data approach, validate the efficacy of tailored methods for detecting synthetic changes in SIB, and reveal that a state-of-the-art LSC model faces challenges in detecting affective dimensions of LSC. This framework provides a valuable tool for dimension- and domain-specific benchmarking and evaluation of LSC methods, with particular benefits for the social sciences. © 2025, CC BY-NC-SA.

关键词： Latent semantic analysis

来源：评论

学校读者我要写书评

暂无评论

Speech-Based Alzheimer's Disease Classification System with Noise-Resilient Features Optimization

Speech-Based Alzheimer's Disease Classification System with ...

引用

Artificial Intelligence and Cognitive Science (AICS), Irish Conference on

作者： Virender Kadyan Puneet Bawa Mohd Mujtaba Akhtar Muskaan Singh Speech and Language Research Centre University of Petroleum and Energy Studies Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Cognitive Analytics Research Lab Intelligent Systems Research Centre School of Computing Ulster University UK

Alzheimer's disease is a severe neurological disorder having a major influence on a substantial portion of the popu-lation. The prompt detection of this condition is crucial, and speech analysis may play a crucial role in facilitating efficient treatment and care. The main aim of this research has been to investigate the significance of timely identification of speech signal abnormalities associated with Alzheimer's disease in order to provide effective therapy interventions and improve disease man-agement. The study used the Mel Frequency Cepstral Coefficients (MFCC) framework, a well recognized technique for feature extraction known for its versatility across several domains. This research introduces an innovative approach that utilizes both individuals diagnosed with dementia and control participants to detect two unique types of cognitive impairment via the analysis of speech signals. The approach used in this work involves the extraction of acoustic properties from pre-processed speech data obtained from the Pitt Corpus of Dementia Bank. This is achieved by using several feature sets, which include a combination of MFCC, prosodic features, and statistical features. This study examines the attributes of optimum feature optimization in actual and noise-enhanced speech environments using machine learning techniques. The integration of MFCC,Statistical and prosodic features has shown remarkable outcomes, exhibiting a superior accuracy rate of 98.3%. This surpasses the performance of other feature combinations when using the Random Forest classifier.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comprehensive Phonological Analysis for Clinical Implication Using Self-Attention Based Grapheme to Phoneme Modeling Under Low-Resource Conditions

Comprehensive Phonological Analysis for Clinical Implication...

引用

Artificial Intelligence and Cognitive Science (AICS), Irish Conference on

作者： Puneet Bawa Virender Kadyan Muskaan Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Speech and Language Research Centre University of Petroleum and Energy Studies Dehradun India Cognitive Analytics Research Lab Intelligent Systems Research Centre School of Computing Ulster University UK

Within the field of speech recognition, a significant obstacle occurs when faced with low-resource situations charac-terized by a scarcity of accessible speech data, which is also het-erogeneous in nature. This becomes more challenging considering the aspect of clinical environments, where accurate transcription of speech is of utmost significance in the identification and man-agement of speech and language disorders. The current manual methodologies used for the development of language models (LMs) and the recognition of speech often encounter difficulties in low-resource scenarios, exhibiting limited ability to adjust to the distinct speech patterns shown by diverse demographics. The present study aims to tackle a significant issue within the field of voice recognition by proposing a solution centered on the advancement of automated language modeling. Specifically, the study highlights the importance of n-gram LMs in this context. The study sheds light on an innovative method that utilizes automated language model development using the multi-head self-attention transformer-based Grapheme-to-Phoneme (G2P) modeling. The results clearly indicate that automated language models outperform humanly created alternatives, highlighting their impressive adaptability and dependability. Furthermore, this research investigates the potential for metamorphosis offered by n-gram language models, resulting in a notable increase in recognition accuracy for the speech recognition system based on the Deep Neural Network-Hidden Markov Model (DNN-HMM).

关键词：

来源：评论

学校读者我要写书评

暂无评论

How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection

arXiv

引用

arXiv 2023年

作者： Guo, Biyang Zhang, Xin Wang, Ziyuan Jiang, Minqi Nie, Jinran Ding, Yuxuan Yue, Jianwei Wu, Yupeng AI Lab School of Information Management and Engineering Shanghai University of Finance and Economics China China School of Information Science Beijing Language and Culture University China School of Electronic Engineering Xidian University China School of Computing Queen’s University Canada Wind Information Co. Ltd China

The introduction of ChatGPT2 has garnered widespread attention in both academic and industrial communities. ChatGPT is able to respond effectively to a wide range of human questions, providing fluent and comprehensive answers that significantly surpass previous public chatbots in terms of security and usefulness. On one hand, people are curious about how ChatGPT is able to achieve such strength and how far it is from human experts. On the other hand, people are starting to worry about the potential negative impacts that large language models (LLMs) like ChatGPT could have on society, such as fake news, plagiarism, and social security issues. In this work, we collected tens of thousands of comparison responses from both human experts and ChatGPT, with questions ranging from open-domain, financial, medical, legal, and psychological areas. We call the collected dataset the Human ChatGPT Comparison Corpus (HC3). Based on the HC3 dataset, we study the characteristics of ChatGPT’s responses, the differences and gaps from human experts, and future directions for LLMs. We conducted comprehensive human evaluations and linguistic analyses of ChatGPT-generated content compared with that of humans, where many interesting results are revealed. After that, we conduct extensive experiments on how to effectively detect whether a certain text is generated by ChatGPT or humans. We build three different detection systems, explore several key factors that influence their effectiveness, and evaluate them in different scenarios. The dataset, code, and models are all publicly available at https://***/Hello-SimpleAI/chatgpt-comparison-detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Linguistics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：