A visual monitoring equipment for coal spontaneous combustion temperature field in goaf is designed，The automatic operation system and self-cleaning device are designed according to the coal mine goaf site，An optimal solution algorithm for temperature anomaly area in goaf is developed and embedded software is formed，The collected data are analyzed through the model to obtain the optimal location of high temperature ignition source in the goaf, and the data are uploaded to the monitoring center through the transmission network.The system formed by the combination of the device and determination method can make visual monitoring and intelligent judgment of the danger area in goaf.
{"title":"Real-time visual monitoring system and method of coal spontaneous combustion temperature field in goaf area","authors":"Zhen Xing","doi":"10.1117/12.2667194","DOIUrl":"https://doi.org/10.1117/12.2667194","url":null,"abstract":"A visual monitoring equipment for coal spontaneous combustion temperature field in goaf is designed，The automatic operation system and self-cleaning device are designed according to the coal mine goaf site，An optimal solution algorithm for temperature anomaly area in goaf is developed and embedded software is formed，The collected data are analyzed through the model to obtain the optimal location of high temperature ignition source in the goaf, and the data are uploaded to the monitoring center through the transmission network.The system formed by the combination of the device and determination method can make visual monitoring and intelligent judgment of the danger area in goaf.","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128383851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nowadays, Alzheimer's Disease (AD) has become a massive problem for middle-aged and older adults. Although due to its long incubation period and early mild symptoms, patients have a more extended period and more possibilities to check out, it is still hard for patients and doctors to diagnose in early routine examinations. This article provides a new method to help the doctor to diagnose Alzheimer's Disease in the early phase. We use transfer learning in deep learning to help diagnose Alzheimer's Disease early in developing Computed Tomography (CT) brain images. Using three pre-trained models, ShuffleNet, DenseNet, and NASNet-mobile as the transfer learning training model and convolution neural networks. We made some improvements to make it more relevant to the actual situation. DenseNet has best performance (87.36%) among the three models. We set the output into four classes: the four stages of Alzheimer's are widely recognized (Mild Demented, Moderate Demented, Very Mild Demented).
{"title":"Harnessing transfer learning for Alzheimer's disease prediction","authors":"Yukun Liu, Chengxuan Zheng, Baha Ihnaini","doi":"10.1117/12.2667247","DOIUrl":"https://doi.org/10.1117/12.2667247","url":null,"abstract":"Nowadays, Alzheimer's Disease (AD) has become a massive problem for middle-aged and older adults. Although due to its long incubation period and early mild symptoms, patients have a more extended period and more possibilities to check out, it is still hard for patients and doctors to diagnose in early routine examinations. This article provides a new method to help the doctor to diagnose Alzheimer's Disease in the early phase. We use transfer learning in deep learning to help diagnose Alzheimer's Disease early in developing Computed Tomography (CT) brain images. Using three pre-trained models, ShuffleNet, DenseNet, and NASNet-mobile as the transfer learning training model and convolution neural networks. We made some improvements to make it more relevant to the actual situation. DenseNet has best performance (87.36%) among the three models. We set the output into four classes: the four stages of Alzheimer's are widely recognized (Mild Demented, Moderate Demented, Very Mild Demented).","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"698 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132780883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The use of virtual reality (VR) in urban area planning is examined in this dissertation. There are numerous allusions to Openstreetmap's urban building data. This VR technology allows users to enter a virtual environment using an input device (HTC Vive). Without actually carrying out the plan, the effectiveness of urban planning can be tested beforehand. VR will become more crucial in urban area planning, boosting urban construction, and assisting critical individuals in making decisions. There were three primary tasks for this project: First task: Create a 3D urban model using the OpenStreetMap map file. Unreal Engine 4 has imported a file with an OSM map (UE4). A data stream has been created to convert OSM files to UE4 uasset files, according to StreepMap and the RuntimeMeshComponent plugin. Second task: Create an overview mode. Users can highlight various items with various features on the overview map in this mode, including different building levels, the top speed limit, hotel star ratings, amenity categories, and retail categories. Third task: Create a VR mode. A virtual environment has been developed to enhance immersion and user experience. The HTC VIVE controller allows users to explore city maps independently. A new interactive interface has been created for this project. With the aid of this method, urban planning initiatives can be pretested before being put into action. Contributions to science: This project's main contribution is developing an application to aid pertinent staff members in managing and planning urban data. Based on this research, urban spatial planning could cut costs by using virtual reality 3D modeling, data integration, and VR interaction.
{"title":"Visualization of planning spaces in virtual reality","authors":"Haiwei Zuo, Wanghui Chu, Bo Yang","doi":"10.1117/12.2667202","DOIUrl":"https://doi.org/10.1117/12.2667202","url":null,"abstract":"The use of virtual reality (VR) in urban area planning is examined in this dissertation. There are numerous allusions to Openstreetmap's urban building data. This VR technology allows users to enter a virtual environment using an input device (HTC Vive). Without actually carrying out the plan, the effectiveness of urban planning can be tested beforehand. VR will become more crucial in urban area planning, boosting urban construction, and assisting critical individuals in making decisions. There were three primary tasks for this project: First task: Create a 3D urban model using the OpenStreetMap map file. Unreal Engine 4 has imported a file with an OSM map (UE4). A data stream has been created to convert OSM files to UE4 uasset files, according to StreepMap and the RuntimeMeshComponent plugin. Second task: Create an overview mode. Users can highlight various items with various features on the overview map in this mode, including different building levels, the top speed limit, hotel star ratings, amenity categories, and retail categories. Third task: Create a VR mode. A virtual environment has been developed to enhance immersion and user experience. The HTC VIVE controller allows users to explore city maps independently. A new interactive interface has been created for this project. With the aid of this method, urban planning initiatives can be pretested before being put into action. Contributions to science: This project's main contribution is developing an application to aid pertinent staff members in managing and planning urban data. Based on this research, urban spatial planning could cut costs by using virtual reality 3D modeling, data integration, and VR interaction.","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122002911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, the ideas of shape template matching, multi-sample template matching, and ROI area processing are combined into an edge contour template matching algorithm module. Based on the OpenCV open source vision library, the area to be removed in the ROI area is colored polygons, and the noise points of this shape feature need to be removed, and only the feature points with obvious edge contour features are extracted; as a result, the matching time is transferred to the mapping time, thereby reducing the total matching time.
{"title":"Research and application of template matching algorithm based on edge contour","authors":"Zhuoxin Liu, Ji Zhang, Kaibin Chu","doi":"10.1117/12.2667677","DOIUrl":"https://doi.org/10.1117/12.2667677","url":null,"abstract":"In this paper, the ideas of shape template matching, multi-sample template matching, and ROI area processing are combined into an edge contour template matching algorithm module. Based on the OpenCV open source vision library, the area to be removed in the ROI area is colored polygons, and the noise points of this shape feature need to be removed, and only the feature points with obvious edge contour features are extracted; as a result, the matching time is transferred to the mapping time, thereby reducing the total matching time.","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"341 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115473964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Image captioning, a cross-modal study, aims to generating a description for a given image, which plays an important role in many fields like image retrieval and computer-assisted instruction. Currently, the challenge in image captioning is the limited quality of generated descriptions including insufficient utilization of image feature information and the limited language learning ability of the decoder. In this paper, we address the above problems by constructing a semantic enhancement module and a multi-round decoding mechanism to enhance the decoding ability of the model, which uses the Transformer model as the primary structure. To validate the efficacy of the model, we conducted intensive experiments on the MSCOCO2014 benchmark and evaluated its performance using five evaluation metrics. The experimental results show that the proposed method in this paper has improved to varying degrees on all five-evaluation metrics.
{"title":"Semantic enhancement methods for image captioning","authors":"Luming Cui, Lin Li","doi":"10.1117/12.2667270","DOIUrl":"https://doi.org/10.1117/12.2667270","url":null,"abstract":"Image captioning, a cross-modal study, aims to generating a description for a given image, which plays an important role in many fields like image retrieval and computer-assisted instruction. Currently, the challenge in image captioning is the limited quality of generated descriptions including insufficient utilization of image feature information and the limited language learning ability of the decoder. In this paper, we address the above problems by constructing a semantic enhancement module and a multi-round decoding mechanism to enhance the decoding ability of the model, which uses the Transformer model as the primary structure. To validate the efficacy of the model, we conducted intensive experiments on the MSCOCO2014 benchmark and evaluated its performance using five evaluation metrics. The experimental results show that the proposed method in this paper has improved to varying degrees on all five-evaluation metrics.","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115351070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yufeng Liu, Yangchen Zhou, Fan Yang, Hongliang Sun
With the development of computer-aided design, visual languages have been widely used as formal methods to represent various types of graphical models. Accordingly, many grammar systems have been proposed for the specification of visual languages. However, apart from shape grammar, most grammars focus on the abstract structures of the languages and ignore the semantic modeling of graph drawing. Furthermore, shape grammar supports generation rather than parsing, with its limited application scope. To address these problems, this paper proposes an enhanced grammar system based on Coordinate Graph Grammar (CGG). Different from traditional grammars, the enhanced system defines a new type of grammatical rule named shape rules to transform graphs into shapes by shape applications. In each shape application, the assertion set describes the range of validity, and shapes can be generated by translation, zoom, and rotation to a set of rule-based coordinates. With the combinations of shape applications and L-applications, the node-edge graph and drawn outline could both be specified, building a bridge between abstract structures and physical layouts of visual languages. An example is given to illustrate the application of the enhanced system in industrial design, where a Bauhaus-style baby cradle is generated by the combination of shape applications and L-applications.
{"title":"An enhanced grammatical approach for graph drawing","authors":"Yufeng Liu, Yangchen Zhou, Fan Yang, Hongliang Sun","doi":"10.1117/12.2667201","DOIUrl":"https://doi.org/10.1117/12.2667201","url":null,"abstract":"With the development of computer-aided design, visual languages have been widely used as formal methods to represent various types of graphical models. Accordingly, many grammar systems have been proposed for the specification of visual languages. However, apart from shape grammar, most grammars focus on the abstract structures of the languages and ignore the semantic modeling of graph drawing. Furthermore, shape grammar supports generation rather than parsing, with its limited application scope. To address these problems, this paper proposes an enhanced grammar system based on Coordinate Graph Grammar (CGG). Different from traditional grammars, the enhanced system defines a new type of grammatical rule named shape rules to transform graphs into shapes by shape applications. In each shape application, the assertion set describes the range of validity, and shapes can be generated by translation, zoom, and rotation to a set of rule-based coordinates. With the combinations of shape applications and L-applications, the node-edge graph and drawn outline could both be specified, building a bridge between abstract structures and physical layouts of visual languages. An example is given to illustrate the application of the enhanced system in industrial design, where a Bauhaus-style baby cradle is generated by the combination of shape applications and L-applications.","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122903008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the problems of poor convenience, poor scalability, and low authentication rate in traditional authentication technology using physical contact authentication methods such as magnetic cards and passwords, this paper explores the accuracy and convenience of the practical application of MR neural network in personal identity authentication. In the MR wearable device, the neural network person identity authentication method is studied flexibly and quickly to detect and identify the person. The 3D information of the face is collected and preprocessed by the depth camera, and the MR identity authentication data set is established. The neural network Resnet model is used for face detection and face feature vector extraction, and the Euclidean method is used to compare the feature vectors and label the characters. The neural network authentication algorithm is mapped to the MR wearable device, and the deep face information in the scene is identified, matched, and labeled by using the unique spatial mapping of MR technology and the camera of the MR wearable device. It solves the problems of low flexibility, poor reliability of face information, and weak recognition stability in traditional identity authentication methods, enabling MR technology to provide a more intelligent identification and labeling method for person identity authentication.
{"title":"Research on identity authentication and labeling technology based on MR neural network","authors":"Hao Yang, Chuan-qian Tang","doi":"10.1117/12.2667283","DOIUrl":"https://doi.org/10.1117/12.2667283","url":null,"abstract":"Aiming at the problems of poor convenience, poor scalability, and low authentication rate in traditional authentication technology using physical contact authentication methods such as magnetic cards and passwords, this paper explores the accuracy and convenience of the practical application of MR neural network in personal identity authentication. In the MR wearable device, the neural network person identity authentication method is studied flexibly and quickly to detect and identify the person. The 3D information of the face is collected and preprocessed by the depth camera, and the MR identity authentication data set is established. The neural network Resnet model is used for face detection and face feature vector extraction, and the Euclidean method is used to compare the feature vectors and label the characters. The neural network authentication algorithm is mapped to the MR wearable device, and the deep face information in the scene is identified, matched, and labeled by using the unique spatial mapping of MR technology and the camera of the MR wearable device. It solves the problems of low flexibility, poor reliability of face information, and weak recognition stability in traditional identity authentication methods, enabling MR technology to provide a more intelligent identification and labeling method for person identity authentication.","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131596935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhen Gu, Da-rong Chen, J. Wang, Chen Dai, Gewei Zhuang
Due to the slow response and poor accuracy of traditional measurement error detection of the electric energy meter, the measurement error detection method of electric energy meter based on machine vision is studied. The minimum error method is used to segment the image threshold to form a binary image. The morphological refinement method is used to extract the image edge contour, combined with machine vision to refine the edge pixels, to achieve the measurement error detection of the instrument. The experimental results show that using the error detection method of machine vision, the detection results are consistent with the error detection results set by the system and the trend is the same. The accuracy also meets the requirements of relevant regulations, which improves the accuracy of electric energy meter measurement.
{"title":"Measurement error detection method of electric energy meter based on machine vision","authors":"Zhen Gu, Da-rong Chen, J. Wang, Chen Dai, Gewei Zhuang","doi":"10.1117/12.2667641","DOIUrl":"https://doi.org/10.1117/12.2667641","url":null,"abstract":"Due to the slow response and poor accuracy of traditional measurement error detection of the electric energy meter, the measurement error detection method of electric energy meter based on machine vision is studied. The minimum error method is used to segment the image threshold to form a binary image. The morphological refinement method is used to extract the image edge contour, combined with machine vision to refine the edge pixels, to achieve the measurement error detection of the instrument. The experimental results show that using the error detection method of machine vision, the detection results are consistent with the error detection results set by the system and the trend is the same. The accuracy also meets the requirements of relevant regulations, which improves the accuracy of electric energy meter measurement.","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131735876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ding-Kuo Huang, Jie Yang, Yunfeng Yan, Xiangwei Sun, Xiaoming Huang
In view of the current image segmentation field, there are few studies on the segmentation of typical wire clamp components of transmission lines. Traditional image processing methods have low segmentation accuracy and require artificial design of feature extraction methods, which are usually only suitable for equipment of a certain structure with insufficient generalization. In this paper, an infrared image segmentation method based on Mask R-CNN (Mask region-based convolutional neural network) for typical guide-ground lines is proposed. Its structure takes Mask R-CNN model combined with FPN (Feature pyramid structure) as the basic framework, and uses RPN (Regional proposal network) to generate candidate regions. Features are extracted from each candidate region through RoI Align layer, and then connected to FC (Fully connected layer) to achieve target classification and bbox (bounding box) regression. A mask branch is also added to predict the segmentation mask. The design can integrate multi-scale and multi-level semantic information to improve the recognition rate when extracting image features. In addition, the network structure is optimized by single channel for infrared images to reduce the size of the model and make it more lightweight. Ablation experiments were performed on two GTX 2080Ti graphics cards to verify the effectiveness of the proposed structure, and the mAP (mean average accuracy) of 0.421 was achieved with an IoU (Intersection over Union) threshold of 0.5.
{"title":"Typical wire clamps segmentation of transmission lines based on infrared image","authors":"Ding-Kuo Huang, Jie Yang, Yunfeng Yan, Xiangwei Sun, Xiaoming Huang","doi":"10.1117/12.2667507","DOIUrl":"https://doi.org/10.1117/12.2667507","url":null,"abstract":"In view of the current image segmentation field, there are few studies on the segmentation of typical wire clamp components of transmission lines. Traditional image processing methods have low segmentation accuracy and require artificial design of feature extraction methods, which are usually only suitable for equipment of a certain structure with insufficient generalization. In this paper, an infrared image segmentation method based on Mask R-CNN (Mask region-based convolutional neural network) for typical guide-ground lines is proposed. Its structure takes Mask R-CNN model combined with FPN (Feature pyramid structure) as the basic framework, and uses RPN (Regional proposal network) to generate candidate regions. Features are extracted from each candidate region through RoI Align layer, and then connected to FC (Fully connected layer) to achieve target classification and bbox (bounding box) regression. A mask branch is also added to predict the segmentation mask. The design can integrate multi-scale and multi-level semantic information to improve the recognition rate when extracting image features. In addition, the network structure is optimized by single channel for infrared images to reduce the size of the model and make it more lightweight. Ablation experiments were performed on two GTX 2080Ti graphics cards to verify the effectiveness of the proposed structure, and the mAP (mean average accuracy) of 0.421 was achieved with an IoU (Intersection over Union) threshold of 0.5.","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134115249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Grammatical error correction (GEC) aims to automatically identify and correct grammatical errors in a sentence. Neural machine translation (NMT) models are the mainstream approaches for the GEC task. However, the models require a large amount of data to be adequately trained, the variety of grammatical errors and the dependencies between errors in a sentence make it difficult for a single NMT model to correct multiple errors at once. In the work, we propose an ensemble approach for heterogeneous models, which integrates rule-based, NMT, and pre-trained language model-based GEC models through the recurrent generation approach, the approach can exploit the strengths of each model and cover a wider range of errors in a sentence. We also mitigate the scarcity of task-specific data for the GEC task through the data augmentation approach. We conduct extensive experiments on the NLPCC2018 shared task dataset to demonstrate the effectiveness of our proposed methods, and reaches the F0.5 value of 37.26, outperforming the best model in the shared task.
{"title":"Heterogeneous models ensemble for Chinese grammatical error correction","authors":"Yeling Liang, Lin Li","doi":"10.1117/12.2667512","DOIUrl":"https://doi.org/10.1117/12.2667512","url":null,"abstract":"Grammatical error correction (GEC) aims to automatically identify and correct grammatical errors in a sentence. Neural machine translation (NMT) models are the mainstream approaches for the GEC task. However, the models require a large amount of data to be adequately trained, the variety of grammatical errors and the dependencies between errors in a sentence make it difficult for a single NMT model to correct multiple errors at once. In the work, we propose an ensemble approach for heterogeneous models, which integrates rule-based, NMT, and pre-trained language model-based GEC models through the recurrent generation approach, the approach can exploit the strengths of each model and cover a wider range of errors in a sentence. We also mitigate the scarcity of task-specific data for the GEC task through the data augmentation approach. We conduct extensive experiments on the NLPCC2018 shared task dataset to demonstrate the effectiveness of our proposed methods, and reaches the F0.5 value of 37.26, outperforming the best model in the shared task.","PeriodicalId":137914,"journal":{"name":"International Conference on Artificial Intelligence, Virtual Reality, and Visualization","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114649217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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