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

作者： Wang, Xiaohua Fei, Wenlong Hu, Min Zhang, Qingyu Zhu, Aoqiang School of Computer Science and Information Engineering HeFei University of Technology China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine China

ISBN: (纸本)9782493814104

The goal of multilingual modelling is to generate multilingual text representations for various downstream tasks in different languages. However, some state-of-the-art pre-trained multilingual models perform poorly on many low-resource languages due to the lack of representation space and model capacity. To alleviate this issue, we propose a Multilingual model Enhanced with Visual Text Representations (MEVTR), which complements textual representations and extends the multilingual representation space with visual text representations. First, the visual encoder focuses on the glyphs and structure of the text to obtain visual text representations, and the textual encoder obtains textual representations. Then, multilingual representations are enhanced by aligning and fusing visual text representations and textual representations. Moreover, we propose similarity constraint, a self-supervised task to prompt the visual encoder to focus on more additional information. Prefix alignment and multi-head bilinear module are designed to acquire an improved integration effect of visual text representations and textual representations. Experimental results indicate that MEVTR benefits from visual text representations and achieves significant performance gains in downstream tasks. In particular, in the zero-shot cross-lingual transfer task, MEVTR achieves results that outperform the state-of-the-art adapter-based framework without the target language adapter. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Signal encoding

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On Simple Perturbation of the α-Universal Triple I Algorithm via Specific Interval Implication Operators 1

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3rd International Conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Tang, Yiming Guo, Anyi Zhang, Li Gao, Jianwei Li, Shujie Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei230601 China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

Fuzzy reasoning aims to describe things with fuzzy and uncertain concepts and convert them into information that computers can process. Wang thought that the inference algorithm based on Compositional Rule of Inference (CRI) had certain defective nature and proposed the triple I algorithm with fully implication operator. Later, the α-universal triple I (α-UTI) algorithm was proposed. As an important part of the fuzzy inference algorithm, the implication operator directly affects the robustness of the inference algorithm, that is, affects the quality of the inference algorithm. Besides, the quality of fuzzy reasoning algorithm can be partly determined by the robustness of reasoning algorithm. In this paper, the ordinary implication operator is extended to a more general interval implication operator. And the simple perturbation of the α-UTI algorithm is studied for some specific interval implication operators under the interval-valued environment. It is observed that the α-UTI algorithm possesses good simple perturbation performance for single rule or multi-rules, in which specific interval implications are used. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Fuzzy inference

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GP-Net: Global Perceptual Network with Adaptive Gradient Clipping for Emotional Image Classification 8

GP-Net: Global Perceptual Network with Adaptive Gradient Cli...

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8th International Conference on intelligent computing and Signal Processing, ICSP 2023

作者： Wang, Xiaohua Yang, Jie Hu, Min Wu, Xia Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350302455

Traditional image emotion recognition focuses only on the emotion information embedded in the subject or part of the image, while ignoring the global emotion information. In this paper, we propose a new network based on Vision Transformer: a global perception network, containing a main module and a background module. The main module enhances the weighting of objects by showing the interdependencies between modelled channels. The background module further searches for emotional information in the background. In addition, we employ multi-task learning and Adaptive Gradient Clipping to improve the network. Extensive experiments show that our proposed method outperforms the state-of-the-art methods for image sentiment analysis. © 2023 IEEE.

关键词： Image classification

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EKIGCN: External Knowledge Injected Graph Convolutional Networks for Aspect-based Sentiment Analysis 9

EKIGCN: External Knowledge Injected Graph Convolutional Netw...

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9th IEEE International Conference on Cloud computing and Intelligence Systems, CCIS 2023

作者： Shi, Xuefeng Hu, Min Ren, Fuji Shi, Piao Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei230000 China University of Electronic Science and Technology of China Sichuan Chengdu611731 China

ISBN: (纸本)9798350304428

Aspect-based sentiment analysis (ABSA) is a challenging subtask in the natural language processing community, which aims to determine the sentiment polarity about the corresponding specific aspect terms. In existing works, external knowledge has been proven effective to improve the ABSA tasks' performance. However, it is still essential to study further to find a universal method for ABSA tasks. Therefore, we propose an external knowledge-injected graph convolutional network (EKIGCN) to enhance the sentiment representation with commonsense. Specifically, Sentic Net is leveraged to provide external knowledge to construct an affective dependency graph. In order to fully exploit the effectiveness of affective knowledge, a shallow mutual interaction is utilized to fuse the learned contextual representation and the graph. Besides, to make EKIGCN sensitive to aspect terms, an aspect-aware multi-head attention module is also employed to enhance the corresponding relations between aspect and contextual words. Extensive experiments are conducted on three popular datasets to validate the effectiveness of our proposed model, and the results outperform the mentioned state-of-the-art methods in ABSA tasks. © 2023 IEEE.

关键词： Sentiment analysis

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Automatic Depression Detection Network Based on Facial Images and Facial Landmarks Feature Fusion

Automatic Depression Detection Network Based on Facial Image...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Hu, Min Xu, Lingxiang Liu, Lei Wang, Xiaohua Li, Hongbo Yang, Jiaoyun Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei China Hefei University of Technology National Smart Eldercare International Science and Technology Cooperation Base Hefei China Hefei University of Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei China

ISBN: (纸本)9798350386226

Artificial intelligence methods offer objectivity and convenience in automatic depression detection, however, current research often neglects the critical role of facial landmarks. This oversight results in insufficient spatial structure information and a lack of detailed local representation, which fails to capture the nuanced semantic information crucial for identifying depression-related clues. To address these issues, we introduce a novel dual-branch network model comprising the Landmark-Image-Landmark Net (LIL Net) and the Global Context Vision Transformer Net (GCVit Net). Through a dual-stream, multiscale, and cross-fusion strategy, LIL Net is designed to extract original facial image features alongside landmark features, prioritizing the detailed semantic information of potential depression clues. LIL Net employs an innovative LIL Attention approach to jointly learn multiscale features from facial landmarks and images, thereby enhancing the model's ability to capture fine-grained depression-related cues. Furthermore, the Multi-scale Feature Fusion (MSFF) module fuses the obtained multiscale features, augmenting the semantic expression of potential depression clues within facial landmarks via attention mechanisms. Meanwhile, the GCVit Net branch network supplements global information by extracting global facial features. Finally, the features from both branches are concatenated to enhance the accuracy of depression degree predictions. Experimental results demonstrate that our model has superior performance in detecting depression compared to existing methods. We release our code at https://***/xlx777/LIL-Net. © 2024 IEEE.

关键词： Artificial intelligence

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On Interval Perturbation of the α-Universal Triple I Algorithm for Unified Interval Implications 1

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3rd International Conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Tang, Yiming Huang, Yifan Qi, Zhiyuan Zhang, Li Gao, Jianwei Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei230601 China College of Overseas Education Nanjing Tech University Nanjing211816 China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

In the fuzzy field, Zadeh put forward the CRI (compositional rule of inference) algorithm to deal with fuzzy reasoning. Later, Wang put forward the triple I algorithm, which is logically more complete than CRI. Thenceforward, Tang found that the harsh conditions of the triple identical implication operators lead to low system performance, so the α-universal triple I (α-UTI) algorithm is proposed. In actual applications, the quality of fuzzy inference algorithm directly is determined by the robustness of inference algorithm. This study researches the interval robustness of the α-UTI algorithm under the interval-valued environment. The interval perturbation performance of the α-UTI algorithm is respectively revealed for the R-interval implication, the triangular modulus interval implication, the S-interval implication and the QL-interval implication. It is observed that the α-UTI algorithm has good interval perturbation performance for single rule or multi-rules, in which unified interval implications are employed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Fuzzy inference

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EmoDamp: A Model for Speaker Emotion Inference in Dialogues with Bidirectional Emotion Damping Mechanism 6

EmoDamp: A Model for Speaker Emotion Inference in Dialogues ...

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6th International Conference on Algorithms, computing and Artificial Intelligence, ACAI 2023

作者： Jiang, Yueqi Sun, Xiao Wang, Jiamin AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei China

ISBN: (纸本)9798400709203

Emotion analysis in conversation has been a popular research topic in the natural language processing field. While much of the existing research has focused on emotion recognition in conversation, the emotion inference task in conversation is more challenging due to its specific prerequisites. In this paper, we propose EmoDamp, a novel model for the conversational emotion inference task. By referring to psychological theories, a bidirectional emotion damping network(BED Net) is designed to model the emotion generation mechanism. Furthermore, external commonsense knowledge is also used to supplement the prediction results to make them more consistent with authentic logic. Finally, a conditional random field (CRF) is used to capture the potential constraints of the emotion transfer process in conversation and simulate emotion contagion. Experimental results on two benchmark datasets demonstrate that EmoDamp outperforms the baseline model, achieving improved results on each emotion category and showcasing its effectiveness. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Human computer interaction

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Temporal Attention Convolution Network for Energy Time Series Forecasting 7

Temporal Attention Convolution Network for Energy Time Serie...

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7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering

作者： Hu, Min Xu, Haijun Wang, Xiaohua Li, Liang Li, Hongbo Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei230009 China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei230009 China MediaTek Hefei230009 China

ISBN: (纸本)9781510656437

Energy time series are characterized by stochasticity, short-term periodicity, and long-term periodicity. Many traditional models such as the autoregressive model, Gaussian process regression are unable to capture the complex time relationships of energy time series. Recurrent neural networks (RNNs), which are widely used for energy time series forecasting, are prone to gradient disappearance or gradient explosion when processing a long sequence data. Thus, RNNs are failed to capture the complete information of the whole long sequence data. To extract complex irregular patterns of energy time series and selectively learn spatio-temporal features, a Temporal Attention Convolution Network (TACN) is proposed in this paper. This structure can extract spatial and temporal features of energy time series, which full filled with our demand. Firstly, to solve the problem that RNNs are prone to information loss when processing the long sequence data, a temporal convolutional network (TCN) is introduced. TCN enables the output to capture the complete information of the whole long sequence data. Then, to reinforce the TCN for extracting effective spatio-temporal features, Squeeze-and-Excitation Network (SENet), a module for computing channel attention, was introduced to this TCN structure. By applying this module, the ability of the acquisition of important channel information has been enhanced. Finally, to further improve the robustness of the model, an autoregressive linear module is constructed to capture the linear relationship among the variables. Experiments on the publicly available datasets Electricity and Solar-Energy show that compare with existing deep learning models, TACN achieves more ideal performance. © 2022 SPIE. All rights reserved.

关键词： Time series

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Finding Emotional Focus for Emotion Recognition at Sentence Level

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Chinese Journal of Electronics 2023年第1期22卷 99-103页

作者： Changqin Quan Fuji Ren Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China Faculty of Engineering The University of Tokushima Tokushima Japan

Emotion recognition at sentence level is one of the fundamental problems of textual emotion understanding. Based on the observation that sentence emotional focus can be expressed by some clauses in this sentence, this paper proposes to find the emotional focus for sentence emotion recognition. For the sake of breaking through the problems brought about by depending on emotion lexicons, we first recognize word emotions in a sentence based on Maximum entropy model. And then homogeneous Markov model is built for clause emotion recognition; After that, a strategy based on emotion selection is proposed for a sentence with multiple clauses, and genetic algorithm is used for clause selection by textual feature weighting. The experimental results show that, comparing with the baseline, there are 9.1% and 3.6% improvement respectively for two different evaluations. It is demonstrated that finding emotional focus by clause selection is able to improve the performance of sentence emotion recognition significantly.

关键词： Emotion recognition Speech recognition Markov processes Entropy Real-time systems Task analysis Research and development

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CoLRP: A Contrastive Learning Abstractive Text Summarization Method with ROUGE Penalty

CoLRP: A Contrastive Learning Abstractive Text Summarization...

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International Joint Conference on Neural Networks (IJCNN)

作者： Caidong Tan Xiao Sun AnHui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of computer and information Hefei University of Technology Hefei China

Contrastive learning can reduce the impact of ex-posure bias associated with training using maximum likelihood estimation, which aims to pull together positive samples to increase the likelihood of high-quality summaries and push away irrelevant negative samples to reduce the likelihood of low-quality summaries. In contrastive learning-based text summarization methods, a standard method for selecting positive and negative samples is randomly selected within a batch. This method can lead to sampling bias to the extent that the consistency of the representation space is compromised. Therefore, we propose a new method to penalize false negatives based on ROUGE metric scores as weights to sample from the dynamic output of the model training process. The method calculates ROUGE metric scores for penalizing false negatives in real-time and can distinguish between positive and negative samples to ensure spatial consistency and alleviate exposure bias. Experimental results on XSum, CNN/DM, and Multi-News datasets show that our approach effectively improves the performance of the latest text summarization pre-training models.

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