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Fenfang Li, Zhengzhang Zhao, Li Wang and Han Deng
Sentence Boundary Disambiguation (SBD) is crucial for building datasets for tasks such as machine translation, syntactic analysis, and semantic analysis. Currently, most automatic sentence segmentation in Tibetan adopts the methods of rule-based and stat...
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Zhenyu Yin, Feiqing Zhang, Guangyuan Xu, Guangjie Han and Yuanguo Bi
Confronting the challenge of identifying unknown fault types in rolling bearing fault diagnosis, this study introduces a multi-scale bearing fault diagnosis method based on transfer learning. Initially, a multi-scale feature extraction network, MBDCNet, ...
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Suping Wang, Ligu Zhu, Lei Shi, Hao Mo and Songfu Tan
Cross-modal retrieval aims to elucidate information fusion, imitate human learning, and advance the field. Although previous reviews have primarily focused on binary and real-value coding methods, there is a scarcity of techniques grounded in deep repres...
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Grigori Sidorov, Fazlourrahman Balouchzahi, Sabur Butt and Alexander Gelbukh
In this paper, we analyzed the performance of different transformer models for regret and hope speech detection on two novel datasets. For the regret detection task, we compared the averaged macro-scores of the transformer models to the previous state-of...
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Yanna Sang, Yuan Chen and Juwei Zhang
Neural machine translation has achieved good translation results, but needs further improvement in low-resource and domain-specific translation. To this end, the paper proposed to incorporate source language syntactic information into neural machine tran...
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Zhuo Wang, Haojie Chen, Hongde Qin and Qin Chen
In the computer vision field, underwater object detection has been a challenging task. Due to the attenuation of light in a medium and the scattering of light by suspended particles in water, underwater optical images often face the problems of color dis...
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Yunfan Gao, Yun Xiong, Siqi Wang and Haofen Wang
Thanks to the development of geographic information technology, geospatial representation learning based on POIs (Point-of-Interest) has gained widespread attention in the past few years. POI is an important indicator to reflect urban socioeconomic activ...
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Samuel Kierszbaum, Thierry Klein and Laurent Lapasset
We consider the problem of solving Natural Language Understanding (NLU) tasks characterized by domain-specific data. An effective approach consists of pre-training Transformer-based language models from scratch using domain-specific data before fine-tuni...
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Teerapong Panboonyuen, Sittinun Thongbai, Weerachai Wongweeranimit, Phisan Santitamnont, Kittiwan Suphan and Chaiyut Charoenphon
Due to the various sizes of each object, such as kilometer stones, detection is still a challenge, and it directly impacts the accuracy of these object counts. Transformers have demonstrated impressive results in various natural language processing (NLP)...
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Turdi Tohti, Mamatjan Abdurxit and Askar Hamdulla
Intent classification and named entity recognition of medical questions are two key subtasks of the natural language understanding module in the question answering system. Most existing methods usually treat medical queries intent classification and name...
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