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检索条件"丛书名=Lecture notes in artificial intelligence,"

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Neurophysiological Correlates of Textual Modulation in Visual Stimuli: An Experimental Study of Russian and English Memes 26th

Neurophysiological Correlates of Textual Modulation in Visua...

引用

26th International Conference on Speech and Computer

作者： Potapova, Rodmonga Potapov, Vsevolod Karimova, Ekaterina Motovskikh, Leonid Obrov, Nikolay Moscow State Linguist Univ Inst Appl & Math Linguist 38 Ostozhenka St Moscow 119034 Russia Lomonosov Moscow State Univ Ctr New Technol Humanities Leninskije Gory 1 Moscow 119991 Russia RAS Lab Appl Physiol Human Higher Nervous Act Inst Higher Nervous Act & Neurophysiol 5A Butlerova St Moscow 117485 Russia

ISBN: (纸本)9783031779602;9783031779619

In this pilot work neurophysiological correlates of textual modulation of perception of visual stimuli using English and Russian-language memes and control stimuli were identified using instrumental neuroimaging methods. Memes constitute a very particular cross-cultural phenomenon, which is a combination of textual and illustrative information, and their effect on the functional state of the brain appears to be little studied. By demonstrating the textual and illustrative part of the memes separately and registering the EEG, we discovered how the text modulates the subsequent perception of the drawing by activating the mechanisms of visual attention. Reading the text in the native language caused a greater response of theta activity in the associative sensory areas of the cortex, which is associated with a better understanding of the meaning of the text. At the same time, the perception of illustrations to English-language memes caused a greater response of theta and alpha rhythms in most of the considered areas of the cortex, which reflects the processes of memory, emotional reaction and the involvement of large neuronal resources for the integration and understanding of the whole image of the meme.

关键词： memes EEG attention modulation context alpha-rhythm

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Multimodal Emotion Recognition Using Compressed Graph Neural Networks 26th

Multimodal Emotion Recognition Using Compressed Graph Neural...

引用

26th International Conference on Speech and Computer

作者： Durkic, Tijana Simic, Nikola Bajovic, Sinisa Suzie Dragana Peric, Zoran Delic, Vladan Univ Novi Sad Fac Tech Sci Novi Sad 21000 Serbia Univ Nis Fac Elect Engn Nish 18000 Serbia

ISBN: (纸本)9783031780134;9783031780141

Since electronic devices have become an integral part of life, there has been a need to bring the communication between a human and a machine closer to being as similar as possible to that between two people. As interpersonal relationships are built on the basis of feelings and empathy, training machines to understand emotions and to provide responses in accordance with the emotional state of the user, i.e. human, has become an interesting area for technology development. To gain a more comprehensive understanding of a person's emotional state, simultaneous utilization of different modalities such as audio, text, and video and their further processing using a graph neural network, recently became popular due to its suitability for tracking a conversation. However, small IoT devices commonly have constrained computational capabilities, memory resources and lower power consumption, and running such a complex multimodal algorithm in real-time may be difficult. In this research, we examine utilization of binarization and 8-bit floating point arithmetic for compressing state-of-the-art GNN-based model COGMEN. We demonstrate that in the case of the multimodal emotion recognition task, such constrained models can provide significant data savings while maintaining relatively high performance, as shown through experiments processing data from the IEMOCAP dataset.

关键词： Graph Neural Networks Emotion Recognition Multimodal Data Compression

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Prompt Debiasing via Causal Intervention for Event Argument Extraction 13th

Prompt Debiasing via Causal Intervention for Event Argument ...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Lin, Jiaju Zhou, Jie Chen, Qin Penn State Univ University Pk PA 16802 USA East China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Prompt-based methods have become increasingly popular among information extraction tasks (e.g., event argument extraction), especially in low-data scenarios. By formatting a fine-tuning task into a pre-training objective, prompt-based methods resolve the data scarce problem effectively. However, previous researches seldom investigate the discrepancy among different strategies on prompt formulation. In this work, we compare two kinds of prompts, name and ontology-based prompts, and reveal how ontology-based prompts exceed its counterpart in event argument extraction. Furthermore, we analyse the potential risk (e.g., biases) in ontology-based prompts via a causal view and propose a debiasing method using causal intervention. Experiments on three benchmarks demonstrate that modified by our debiasing method, the baseline model becomes more robust, with significant improvement in the resistance to adversarial attacks. (1)Our code is available at this repository.

关键词： Event Argument Extraction Prompt Learning Causal Intervention

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Chinese Named Entity Recognition Based on Template and Contrastive Learning 13th

Chinese Named Entity Recognition Based on Template and Contr...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Zhu, Jingjing Cai, Tianyu Zhao, Zhenyu Ju, Shenggen Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Introducing lexicon knowledge into character-level models enhances their ability to discern word boundaries, thus boosting the model's performance. Although the above methods have significantly improved the performance of the model, there are still the following issues. The existing character-level models can only obtain word boundary information from potential lexicon generated from text, and special word information that does not appear in the lexicon cannot be perceived and obtained. Therefore, the problem of out-of-vocabulary words in the lexicon will reduce the performance of the model. To address this, we propose a Chinese Named Entity Recognition Based on Template and Contrastive Learning model(TBCL). This model segments input sentences into n-gram spans and constructs prompt templates to capture comprehensive word information. Leveraging contrastive learning, we designed a triplet loss function to efficiently utilize prompt templates for learning word boundary information. The model is tested on three Chinese named entity recognition datasets: Resume, MSRA and Ontonotes, and the experiment results obtained are superior to the baseline models, which fully prove the effectiveness of the proposed model.

关键词： Chinese named entity recognition Prompt learning Contrastive learning

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TGAN: Temporal-Aware Graph Attention Network for Early Rumor Detection in Social Media 13th

TGAN: Temporal-Aware Graph Attention Network for Early Rumor...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Zhang, Shubo Wei, Jing Zhao, Zhengyi Li, Binyang Wong, Kam-Fai Univ Int Relat Sch Cyber Sci & Engn Lab Cognit Intelligence & Secur Beijing Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China MoE Key Lab High Confidence Software Technol Beijing Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Threatening rumors diffuse more quickly and widely than ever with the popularity of social media. Therefore, it is crucial to identify a rumor as early as possible to stop its spreading and reduce the potential damages. Mainstream social media rumor detection methods utilize the content or propagation information but ignore the global temporal information, i.e., timestamps of published posts, therefore fail in capturing the variant attentions of the interactive pattern on different time intervals and determining a dynamical time point to debunk for each individual rumor. Meanwhile, the absence of global temporal information cannot provide a whole picture of the rumor propagation structure and will reduce the robustness of the model. For this purpose, we present a novel Temporal-aware Graph Attention Network (TGAN), for early rumor detection. In TGAN, the cascade of a rumor can be represented with a graph neural network, where each node denotes a post while the edge denotes the interactive pattern among posts. The global temporal information is integrated in TGAN, (1) to capture neighborhood attention and interactive attention to represent the structural information;(2) to learn the potential influence of a post and estimate the burst time of a rumor for early rumor detection. More importantly, the rumor can be identified earlier before the burst time, while maintaining a promising accuracy.

关键词： Social media Early rumor detection Global temporal information Graph attention network

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CSLAN: A Novel Lexicon Attention Network for Chinese NER 13th

CSLAN: A Novel Lexicon Attention Network for Chinese NER

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Lin, Rongsheng Gai, Shubin Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Lab Artificial Intelligence & Digital Econ Shenzh Shenzhen Peoples R China Shenzhen Technol Univ Coll Big Data & Internet Shenzhen Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Recently, many studies have employed lexicon-based models to integrate lexicon information into character repersentation for Chinese Named Entity Recognition(NER) tasks, and these models have demonstrated high effectiveness. However, previous approaches to incorporate lexicon information into character representations calculate the correlation solely between the current character and the matching words. They neglect the words matched by neighboring characters, leading to an underutilization of lexicon information in character sequences. To address this issue, we introduce a novel lexical attention network (CSLAN). This network improves character representation by incorporating a new lexical attention mechanism. This mechanism enables the current character to consider the lexicon information from neighboring characters when fusing it into the character representation, thereby regulating attention between the character and the associated vocabulary. We conduct extensive experiments, and results show that our proposed CSLAN achieves competitive results on four publicly available datasets.

关键词： Chinese Named Entity Recognition Lexicon Enhancement Attention Mechanism

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Multi-layer Sequence Labeling-Based Joint Biomedical Event Extraction 13th

Multi-layer Sequence Labeling-Based Joint Biomedical Event E...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Chen, Gongchi Wu, Pengchao Gu, Jinghang Qian, Longhua Zhou, Guodong Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Peoples R China Hong Kong Polytech Univ Dept Chinese & Bilingual Studies Hong Kong 999077 Peoples R China

ISBN: (纸本)9789819794331;9789819794348

In recent years, biomedical event extraction has been dominated by complicated pipeline and joint methods, which need to be simplified. In addition, existing work has not effectively utilized trigger word information explicitly. Hence, we propose MLSL, a method based on multi-layer sequence labeling for joint biomedical event extraction. MLSL does not introduce prior knowledge and complex structures. Moreover, it explicitly incorporates the information of candidate trigger words into the sequence labeling to learn the interaction relationships between trigger words and argument roles. Based on this, MLSL can learn well with just a simple workflow. Extensive experimentation demonstrates the superiority of MLSL in terms of extraction performance compared to other state-of-the-art methods.

关键词： Biomedical event extraction Sequence labeling Natural language processing

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MCKRL: A Multi-channel Based Multi-graph Knowledge Representation Learning Model 13th

MCKRL: A Multi-channel Based Multi-graph Knowledge Represent...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Tang, Zihao Zhang, Xiang Shang, Xiaoyu Southeast Univ Sch Software Engn Nanjing Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Knowledge representation learning (KRL) aims to encode entities and relationships from the real world into low-dimensional, real-valued vectors. However, knowledge in specific domains is often distributed across multiple graphs, each complementing the others, posing challenges to current mainstream methods, which often fail to fully exploit the heterogeneity and complementarity of information across graphs. To address this issue, We propose MCKRL for multi-graph KRL, including two components to address the heterogeneity and complementarity of knowledge, respectively: (1) A hierarchical attention mechanism to convert diverse attributes into uniform-dimensional vectors, fusing them with attention from both attribute and source levels. (2) Introducing three channels (single graph, sub-graph, and combined graph), employing graph auto-encoders to learn entity embeddings in each channel, and combining them with graph-level attention for link prediction evaluation. Extensive experiments conducted on two representative knowledge graphs in biomedical research demonstrate the superior performance of MCKRL over the state-of-the-arts. The implementations are available at: https://***/wds-seu/MCKRL.

关键词： Knowledge Graphs Representation Learning Graph Neural Networks Link Prediction

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Improving Self-consistency for Open-Domain Question Answering via Automatic Prompt Engineering and Ensemble Learning 13th

Improving Self-consistency for Open-Domain Question Answerin...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Li, Jie Han, Xue Deng, Chao Feng, Junlan China Mobile Res Inst JiuTian Team Beijing Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Large Language Models (LLMs) have shown remarkable performance on a variety of natural language tasks, but eliciting their abilities for more consistent predictions is still highly reliant on researchers' well-crafted prompts, which often require costly computational consumption as well as onerous trial-and-error efforts. For generative tasks such as open-domain question answering (QA), LLMs face a particularly great challenge. To alleviate the issue in QA tasks, we propose a scheme that can both automatically optimize and efficiently leverage candidate prompts to obtain consistent and accurate predicted answers. Superior self-consistency of LLMs' predictions requires diverse high-quality reasoning paths, thus multiple candidate prompts in the iterative optimization process of automatic prompt engineering (APE) are fully leveraged to drive LLMs to generate multiple reasoning paths leading to improved self-consistency. The evaluation performance of the candidate prompt on the sampled dataset is used as a reference (similar to the weights of the base learners in ensemble learning) to determine its contribution to the final answer. Experimental results demonstrate that our scheme yields more reliable and diverse predicted answers and outperforms conventional self-consistency baseline models in several typical QA benchmark tests.

关键词： Question answering Self-consistency Prompt learning Ensemble learning

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Leveraging Parameter-Efficient Fine-Tuning for Multilingual Abstractive Summarization 13th

Leveraging Parameter-Efficient Fine-Tuning for Multilingual ...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Shen, Jialun Wang, Yusong Tokyo Inst Technol Meguro Japan Guangdong Inst Intelligence Sci & Technol Zhuhai Peoples R China

ISBN: (纸本)9789819794362;9789819794379

In recent years, parameter-efficient fine-tuning methods have gained attention as an alternative to full fine-tuning of pre-trained language models for transfer learning. Such methods can alleviate the problem that full fine-tuning requires updating and storing all the model parameters for different tasks, showing comparable performance with much fewer tuned parameters. However, their effectiveness in the context of multilingual natural language processing remains underexplored. This paper evaluates the performance of two representative parameter-efficient fine-tuning methods, prefix-tuning and LoRA, on multilingual abstractive summarization, comparing them with traditional full fine-tuning approach. Our comprehensive analysis highlights the trade-offs between efficiency and performance, providing benchmarks to standardize evaluation in this domain. Additionally, we delve into an in-depth examination of prefix-tuning, particularly under few-shot conditions, uncovering insights into its efficacy and offering guidance for optimizing its performance in multilingual environments. Our findings contribute to a deeper understanding of the applicability and benefits of parameter-efficient fine-tuning methods in multilingual natural language processing. This research aims to inform future developments and encourage the adoption of parameter-efficient fine-tuning techniques in multilingual contexts, ultimately enhancing the performance and scalability of pre-trained language models. We have made our source code publicly available at https://***/sgallon-rin/peft-mas.

关键词： Transfer Learning Parameter-efficient Fine-tuning Multilingual Abstractive Summarization Pre-trained Language Models

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