检索结果-内蒙古大学图书馆

Constructing a Chinese-Vietnamese bilingual corpus from subtitle websites

International Journal of Intelligent Information and Database Systems 2024年第4期16卷 385-408页

作者： Nguyen, Phuc-Nghi Tran, Phuoc Natural Language Processing and Knowledge Discovery Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam

In this work, we introduce a method of constructing a Chinese-Vietnamese bilingual corpus on subtitle resources. The corpus construction process involved careful curation and preprocessing of the chosen subtitle data to ensure its suitability for training and evaluating machine translation models. We applied rigorous quality control measures to enhance the reliability and relevance of the collected corpus by systematically eliminating entries that did not meet a predetermined level of correctness. We use the two robust neural machine translation models to experiment on the collected corpus. The experimental results show that the highest BLEU score of the collected corpus is 22.0, much higher than the OpenSubtitles 2016 corpus – one of the most popular subtitle corpus today. By curating a specialised corpus, we aim to contribute valuable resources to the field of machine translation, fostering advancements in the understanding and improvement of translation quality between Chinese and Vietnamese. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Neural machine translation

来源：评论

学校读者我要写书评

暂无评论

A Framework for Vietnamese Question-Answering in Law Domain 9

A Framework for Vietnamese Question-Answering in Law Domain

引用

9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Le, Thang V. Q. Vu, Dinh-Hong Pham, Van-Huy Le, Anh-Cuong Nguyen, Nguyen P. Ton Duc Thang University Natural Language Processing and Knowledge Discovery Laboratory Faculty of Information Technology Ho Chi Minh City Viet Nam Ton Duc Thang University Faculty of Information Technology Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350391367

The popularity of building question-answering systems using Large language Models (LLMs) has surged. Many projects leverage the Retrieval Augmented Generated (RAG) technique, involving two basic steps: retrieval and reading. In this research, we introduce an enhanced approach, termed CRRR (Classifier - Rewrite - Retriever - Reader), tailored for the Vietnamese legal domain. Our framework begins with a classifier to discern whether a given question pertains to law. Rather than solely focusing on refining LLM or embedding models for better responses, we prioritize enhancing the process of rewriting input questions. These rewritten queries are then utilized by a search engine to gather external information, aiding the reader in generating answers. The rewriter component is trainable using reinforcement learning, incorporating feedback from both human and AI sources. © 2024 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Sharing Parameter by Conjugation for knowledge Graph Embeddings in Complex Space 16

Sharing Parameter by Conjugation for Knowledge Graph Embeddi...

引用

16th Workshop on Graph-Based Methods for natural language processing, TextGraphs 2022, in conjunction with the 29th International Conference on Computational Linguistics, COLING 2022

作者： Feng, Xincan Qu, Zhi Cheng, Yuchang Watanabe, Taro Yugami, Nobuhiro Natural Language Processing Laboratory Nara Institute of Science and Technology Japan Multilingual Knowledge Computing Laboratory Fujitsu Ltd Japan

A knowledge Graph (KG) is the directed graphical representation of entities and relations in the real world. KG can be applied in diverse natural language processing (NLP) tasks where knowledge is required. The need to scale up and complete KG automatically yields knowledge Graph Embedding (KGE), a shallow machine learning model that is suffering from memory and training time consumption issues. To mitigate the computational load, we propose a parameter-sharing method, i.e., using conjugate parameters for complex numbers employed in KGE models. Our method improves memory efficiency by 2x in relation embedding while achieving comparable performance to the state-of-the-art non-conjugate models, with faster, or at least comparable, training time. We demonstrated the generalizability of our method on two best-performing KGE models 5*E (Nayyeri et al., 2021) and ComplEx (Trouillon et al., 2016) on five benchmark datasets. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Directed graphs

来源：评论

学校读者我要写书评

暂无评论

A Method of Integrating Length Constraints into Encoder-Decoder Transformer for Abstractive Text Summarization

引用

Intelligent Automation & Soft Computing 2023年第10期38卷 1-18页

作者： Ngoc-Khuong Nguyen Dac-Nhuong Le Viet-Ha Nguyen Anh-Cuong Le Faculty of Information Technology Haiphong UniversityHaiphong180000Vietnam Faculty of Information Technology VNU University of Engineering and TechnologyHanoi10000Vietnam Natural Language Processing and Knowledge Discovery Laboratory Faculty of Information TechnologyTon Duc Thang UniversityHo Chi Minh CityVietnam

Text summarization aims to generate a concise version of the original *** longer the summary text is,themore detailed it will be fromthe original text,and this depends on the intended ***,the problem of generating summary texts with desired lengths is a vital task to put the research into *** solve this problem,in this paper,we propose a new method to integrate the desired length of the summarized text into the encoder-decoder model for the abstractive text summarization *** length parameter is integrated into the encoding phase at each self-attention step and the decoding process by preserving the remaining length for calculating headattention in the generation process and using it as length embeddings added to theword *** conducted experiments for the proposed model on the two data sets,Cable News Network(CNN)Daily and NEWSROOM,with different desired output *** obtained results show the proposed model’s effectiveness compared with related studies.

关键词： Length controllable abstractive text summarization length embedding

来源：评论

学校读者我要写书评

暂无评论

Towards a Human-like Chatbot using Deep Adversarial Learning 14

Towards a Human-like Chatbot using Deep Adversarial Learning

引用

14th International Conference on knowledge and Systems Engineering, KSE 2022

作者： Tran, Quoc-Dai Luong Le, Anh-Cuong Huynh, Van-Nam Ton Duc Thang University Natural Language Processing and Knowledge Discovery Laboratory Faculty of Information Technology Ho Chi Minh City Viet Nam School of Knowledge Science Japan Advanced Institute of Science and Technology Japan

ISBN: (数字)9781665452816

ISBN: (纸本)9781665452809

Conversational agents are getting more popular and applied in a wide range of practical application areas. The main task of these agents is not only to generate context-appropriate responses to a given query but also to make the conversation human-like. Thanks to the ability of deep learning based models in natural language modeling, recent studies have made progress in designing conversational agents that can provide more semantically accurate responses. However, the naturalness in such conversation setting has not been given adequate attention in these studies. This paper aims to incorporate both important criteria of accuracy and naturalness of conversation in developing a new model for conversational agents. To this end, inspired by the idea of Turing test and the idea of adversarial learning strategy, we propose to design a model based on generative deep neural networks that interestingly allow to generate accurate responses optimized by the mechanics of imitating human-generated conversations. Experimental results demonstrate that the proposed models produce more natural and accurate responses, yielding significant gains in BLEU scores. © 2022 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Sharing Parameter by Conjugation for knowledge Graph Embeddings in Complex Space

arXiv

引用

arXiv 2024年

A knowledge Graph (KG) is the directed graphical representation of entities and relations in the real world. KG can be applied in diverse natural language processing (NLP) tasks where knowledge is required. The need to scale up and complete KG automatically yields knowledge Graph Embedding (KGE), a shallow machine learning model that is suffering from memory and training time consumption issues. To mitigate the computational load, we propose a parameter-sharing method, i.e., using conjugate parameters for complex numbers employed in KGE models. Our method improves memory efficiency by 2x in relation embedding while achieving comparable performance to the state-of-the-art non-conjugate models, with faster, or at least comparable, training time. We demonstrated the generalizability of our method on two best-performing KGE models 5★E (Nayyeri et al., 2021) and ComplEx (Trouillon et al., 2016) on five benchmark datasets. © 2024, CC BY.

关键词： knowledge graph

来源：评论

学校读者我要写书评

暂无评论

A Local Attention-based Neural Networks for Abstractive Text Summarization 23

A Local Attention-based Neural Networks for Abstractive Text...

引用

5th Asia Pacific Information Technology Conference, APIT 2023

作者： Nguyen, Ngoc-Khuong Nguyen, Viet-Ha Le, Anh-Cuong Hai Phong University Faculty of Information and Technology Viet Nam Ai Institute Vnu University of Engineering and Technology Faculty of Information Technology Viet Nam Natural Language Processing and Knowledge Discovery Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh Viet Nam

ISBN: (纸本)9781450399500

Text summarization is a process of generating a concise summary of a given text. It is a popular research topic, and many studies have used sequence-to-sequence deep neural network models such as Long Short Term Memory (LSTM) or Gated Recurrent Unit (GRU) to tackle it. These models consist of two phases: encoding the input text and generating the summary. However, these models may lose information during the encoding phase, particularly when using deep layers, leading to inaccurate summaries. In this paper, we propose a bi-directional LSTM model with a Recurrent Residual Attention mechanism to address this issue. We tested our model on the Amazon Reviews dataset from the Stanford Network Analysis Project and found that it performed better than standard LSTM models and outperformed previous studies. © 2023 ACM.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

A deep reinforcement learning model using long contexts for chatbots

A deep reinforcement learning model using long contexts for ...

引用

2021 International Conference on System Science and Engineering, ICSSE 2021

作者： Tran, Quoc-Dai Luong Le, Anh-Cuong Ton Duc Thang University Natural Language Processing and Knowledge Discovery Laboratory Faculty of Information Technology Ho Chi Minh City Viet Nam

ISBN: (纸本)9781665448482

Recently, deep neural network (DNN) based chat-bots offer great promise for generating responses. With architecture using an encoder-decoder integrated with attention mechanism, they showed a good one for generating natural responses, however they still are be short-sighted in predicting response one at a time while ignoring their influence on future outcomes. This is one of the reason DNN based models usually tend to generate responses not relevant to previous responses. In this paper, we show how to apply reinforcement learning to those models that can help them achieve a specific goal. By simulating dialogues between two pretrained chatbots with DNN, followed by using policy gradient methods to reward sequences. We also present our forward-looking function for desirable goal which will be used to improve the models. The experimental results show that the proposed model generates appropriate responses to content with more information relevant to the conversation context. In addition, our model improves up to 43% BLEU score compared to the baseline. © 2021 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Deep Learning Approach for PM2.5 Concentration Prediction in Smart Environmental Monitoring

引用

Intelligent Automation & Soft Computing 2023年第6期36卷 3029-3041页

作者： Minh Thanh Vo Anh HVo Huong Bui Tuong Le Faculty of Information Technology HUTECH UniversityHo Chi Minh CityVietnam Natural Language Processing and Knowledge Discovery Laboratory Faculty of Information TechnologyTon Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Computing Fundamentals FPT UniversityHo Chi Minh CityVietnam Laboratory for Artificial Intelligence Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh CityVietnam Faculty of Information Technology Van Lang UniversityHo Chi Minh CityVietnam

Nowadays,air pollution is a big environmental problem in develop-ing *** this problem,particulate matter 2.5(PM2.5)in the air is an air *** its concentration in the air is high in developing countries like Vietnam,it will harm everyone’s *** prediction of PM2.5 concentrations can help to make the correct decision in protecting the health of the *** study develops a hybrid deep learning approach named PM25-CBL model for PM2.5 concentration prediction in Ho Chi Minh City,***,this study analyzes the effects of variables on PM2.5 concentrations in Air Quality HCMC *** variables that affect the results will be selected for PM2.5 concentration ***,an efficient PM25-CBL model that integrates a convolutional neural network(CNN)andBidirectionalLongShort-TermMemory(Bi-LSTM)*** model consists of three following modules:CNN,Bi-LSTM,and Fully connected ***,this study conducts the experiment to compare the performance of our approach and several state-of-the-art deep learning models for time series prediction such as LSTM,Bi-LSTM,the combination of CNN and LSTM(CNN-LSTM),and *** empirical results confirm that PM25-CBL model outperforms other methods for Air Quality HCMC dataset in terms of several metrics including Mean Squared Error(MSE),Root Mean Squared Error(RMSE),Mean Absolute Error(MAE),and Mean Absolute Percentage Error(MAPE).

关键词： Time series prediction PM2.5 concentration prediction CNN Bi-LSTM network

来源：评论

学校读者我要写书评

暂无评论

Towards Information Sharing Beetle Antennae Search Optimization 24th

Towards Information Sharing Beetle Antennae Search Optimizat...

引用

24th International Conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Liu, Xuan Wang, Chenyan Liu, Wenjian Zhang, Lefeng Liu, Xianggan Gao, Yutong Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of China Beijing China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan China City University of Macau China Natural Language Processing and Knowledge Graph Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9789819615278

The study of bioinformatics-based evolutionary computation has long been of significant interest within the scientific community. Beetle antennae search algorithm is widely used because of its lightweight, however, it lacks information sharing due to individual iteration in the algorithm. In this paper, we propose an innovative pheromone-based beetle antennae search algorithm, which evolves from a single iterative individual in BAS algorithm to multiple parallel iterative individuals and incorporates the pheromone sharing mechanism found in ant colony optimization algorithm. Applying the pheromone-based beetle antennae search algorithm to the virtual machine placement problem in cloud computing, we find that pheromone sharing mechanism allows the PB-BAS algorithm to exhibit superior optimization capabilities and effectively avoids convergence to local optimal. To verify the performance of the algorithm, we select other alternative algorithms and conduct a large number of comparative experiments under different experimental setups, the experimental results show the effectiveness and efficiency of our algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ant colony optimization

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：