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

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Neural Chat Translation as Online Document-to-Document Translation 13th

Neural Chat Translation as Online Document-to-Document Trans...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Lyu, Mengzhe Dou, Huaixia Li, Junhui Zhu, Muhua Zhou, Guodong Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China Meituan Youxuan Business Unit Beijing Peoples R China

ISBN: (纸本)9789819794362;9789819794379

Neural chat translation (NCT) aims to translate utterances in a bilingual dialogue, where participants speak different languages, into their respective native languages. Recent advances on NCT translate chat utterances one by one using document-to-sentence (Doc2Sent) models, associated with deliberately designed auxiliary tasks that explicitly model the inherent bilingual conversational characteristics. Different from Doc2Sent models, in this paper we simply view chat translation as online document-to-document (Doc2Doc) translation, in which both the input and output are bilingual. Moreover, in order to alleviate the challenges that the encoder and decoder may encounter in grasping semantics with bilingual context, we propose a cross-lingual regularization which regularizes the translation prediction between the default bilingual-input translation and two auxiliary monolingual-input translations. Experimental results on *** and *** chat translation tasks show that based on G-Transformer, a state-of-the-art Doc2Doc translation model, our approach outperforms sentence-level baselines and many existing models.

关键词： Neural chat translation Document-to-Document translation Bilingual dialogue Cross-lingual regularization

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Optimized Conversational Gesture Generation with Enhanced Motion Feature Extraction and Cascaded Generator 13th

Optimized Conversational Gesture Generation with Enhanced Mo...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Wang, Xiang Peng, Yifeng Liu, Zhaoxiang Dong, Shijie Liu, Ruitao Wang, Kai Lian, Shiguo China Unicom AI Innovat Ctr Beijing 100013 Peoples R China China Unicom Unicom Digital Technol Beijing 100013 Peoples R China

ISBN: (纸本)9789819794362;9789819794379

Conversational gesture generation holds pivotal importance in enhancing the natural flow of digital human interaction. However, the inherent weak correlation between gestures and language poses a significant challenge for automated gesture generation systems. Existing methods have shortcomings in the quality of generated gestures and inference speed. In this paper, we propose an optimized method for conversational gesture generation. By training a more powerful motion feature extraction network, the connotation of different motions can be accurately grasped. Moreover, we utilize a two-level cascaded generator, which initially produces broad movements such as arm gestures, followed by generating finer gestures like finger movements. To enhance network convergence and stability, we integrate multiple loss terms and implement refined training strategies. These measures collectively contribute to the improved quality of the final generated gestures. Additionally, we employ GRU as the backbone network, striking a balance between generation quality and computational efficiency. Both subjective and objective experiments demonstrate that our method achieves a remarkable inference speed while generating high-quality gestures.

关键词： Conversational gestures synthesis Motion feature extraction Cascaded gesture generator

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Essay Coherence Evaluation and Feedback Enhanced by Semi-supervised Learning and Auxiliary Information 13th

Essay Coherence Evaluation and Feedback Enhanced by Semi-sup...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Li, Chenyang Zhang, Long Guo, Hui Zheng, Qiusheng Cent China Normal Univ Fac Artificial Intelligence Educ Wuhan Peoples R China Zhongyuan Univ Technol Sch Cyberspace Secur Zhengzhou Peoples R China

ISBN: (纸本)9789819794423;9789819794430

According to the grading standards for middle school examinations, thought content and structural coherence are critical indicators of essay quality. Evaluating an essay's coherence requires not only analyzing the semantic connections between sentences but also understanding the logical relationships between sentences and the overall structural layout of the essay. This understanding is essential for grasping the logical framework of long texts and generating complex documents. This paper presents our approach used in the NLPCC 2024 competition for evaluating essay sentence logic and generating feedback. For different tasks, we applied both traditional classification models and advanced large language models, leveraging pseudo-labels generated through semi-supervised learning and information provided by large models as auxiliary data, significantly enhancing overall model performance. Experimental results demonstrate that our method achieves state-of-the-art results.

关键词： Essay Quality Assessment Semi-Supervised Learning Large Language Models Auxiliary Data

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Approach to Assessing the Quality of Syllable Pronunciation by Patients in the Process of Speech Rehabilitation Based on Comparison with Healthy Speakers 26th

Approach to Assessing the Quality of Syllable Pronunciation ...

引用

26th International Conference on Speech and Computer

作者： Egle, German Novokhrestova, Dariya Tomilina, Svetlana Kostyuchenko, Evgeny Tomsk State Univ Control Syst & Radioelect Lenina St 40 Tomsk 634050 Russia

ISBN: (纸本)9783031779602;9783031779619

The article proposes a modification of the methodology for assessing the quality of pronunciation of a patient's syllables in the process of speech rehabilitation. A modification of the technique consists in applying an approach that allows the use of recordings of healthy speakers to assess the quality of pronunciation of various phonemes. To assess the quality of pronunciation, we use the distance between two files with recordings in wav format;the smaller the distance between two files, the more similar they are considered. Distance calculation methods discussed included DTW, EDR, ERP, LCSS, MSM, and Euclidean distance. The accuracy of the method was tested using values obtained using the instantaneous energy envelope and the Gilbert envelope, in the first case ERP was the most accurate method, in the second EDR. It was revealed that the LCSS and ERP methods perform calculations much longer than other methods and do not have high accuracy, as a result, they are not included in the list of the best calculation methods. The hypothesis that the two means were equal was also tested using the Mann-Whitney method to confirm that the distance between the healthy speaker recording and the preoperative patient recording differs from the distance between the healthy speaker recording and the recording of the patient after surgery. As a result, it turned out that the most accurate and fastest method for calculating the distance between two records is EDR. Analysis of the results obtained showed that this approach is applicable to solving the problem of speech analysis, but requires significant improvements and subsequent research due to the low accuracy of the put forward theories.

关键词： Speech Assessment Speech Rehabilitation Healthy Speaker Assessment Algorithm

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GSEA: Global Structure-Aware Graph Neural Networks for Entity Alignment 13th

GSEA: Global Structure-Aware Graph Neural Networks for Entit...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Wang, Cunda Wang, Weihua Liang, Qiuyu Yu, Jie Gao, Guanglai Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China Natl & Local Joint Engn Res Ctr Intelligent Infor Hohhot Peoples R China Inner Mongolia Key Lab Multilingual Artificial In Hohhot Peoples R China Natl Univ Def Technol Coll Comp Sci Changsha Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Entity alignment (EA) aims to identify the same real-world entities presented in different knowledge graphs (KGs), which is the most crucial step in integrating multi-source KGs. Existing entity alignment methods based on Graph Neural Networks mainly focus on aggregating neighborhood structure information within the original graph to generate entity embeddings. However, these methods fail to effectively leverage the global structural information of entities. In this paper, we propose a novel method based on Global Structure-Aware Graph Neural Networks to capture the global structure between entities. Specifically, our method learns global embeddings from the augmented graph reconstructed by Singular Value Decomposition. Moreover, we employ contrastive learning to maximize the consistency between global and local embeddings while mitigating the over-smoothing problem. Extensive experimental results on benchmark datasets, along with further analysis, demonstrate the superiority and effectiveness of our method. The source code is available at https://***/wonderCS1213/GSEA.

关键词： Knowledge graph Entity alignment Graph neural networks Contrastive learning

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CETA: Context-Enhanced and Target-Aware Hateful Meme Inference Method 13th

CETA: Context-Enhanced and Target-Aware Hateful Meme Inferen...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Wang, Kaichun Lu, Junyu Yu, Bingjie Yang, Liang Lin, Hongfei Dalian Univ Technol Coll Comp Sci & Technol Dalian Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Hateful memes spread quickly online and harm society, necessitating effective detection methods. Detecting these memes is challenging due to the need for comprehensive reasoning. Though great efforts have been made, existing detection methods overlook the specific target of hateful meme, resulting in inadequate meme comprehension and hindering performance. In this paper, we propose Context-Enhanced and Target-Aware Hateful Meme Inference Method (CETA), which detects hateful memes while simultaneously pays attention to their targets. Specifically, CETA employs a prompting template to integrate contextual information and guide the model in reasoning the meme's target and assessing its hatefulness based on the context. Experimental results show that our approach outperforms the state-of-the-art baselines on two hateful meme datasets, achieving up to 1.82% improvement in accuracy, significantly enhancing model inference and detection performance.

关键词： Hateful Memes Multimodal Reasoning Prompt Learning PLMs

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TiLTS:Tibetan Long Text Summarization Dataset 13th

TiLTS:Tibetan Long Text Summarization Dataset

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Hao, Yanrong Chen, Bo Zhou, Xiaobing Minzu Univ China Sch Informat Engn Beijing Peoples R China Natl Language Resources Monitoring & Res Ctr Mino Beijing Peoples R China

ISBN: (纸本)9789819794393;9789819794409

High-quality datasets are crucial for advancing research in automatic text summarization. At present, summarization models for resource-rich languages like Chinese and English have made significant progress. However, for low-resource languages such as Tibetan, the lack of large-scale publicly available summarization datasets means that related research is still in its early stages. To address this gap, this paper constructs an open Tibetan summarization dataset, TiLTS. By collecting extensive texts from Tibetan news websites and leveraging resources from other languages, it obtains 36,507 (document, summary) pairs. Compared to the only publicly available Tibetan summarization dataset, TiSUM, TiLTS has clear advantages in both data size and challenge. This paper also conducts experiments and analyses using several summarization algorithms on this dataset.

关键词： Text summarization Dataset Tibetan Low-resource

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A Privacy-Preserving Framework for Medical Chatbot Based on LLM with Retrieval Augmented Generation 13th

A Privacy-Preserving Framework for Medical Chatbot Based on ...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Li, Yufei Li, Chunshan Wang, Zecheng Sui, Dianbo Yan, Jianen Harbin Inst Technol Weihai Peoples R China

ISBN: (纸本)9789819794362;9789819794379

With the continuous advancement of Large Language Models (LLMs) and generative AI technologies, an increasing number of organizations are customizing and fine-tuning LLMs with internal data and documents to enhance the efficiency and reliability of data-driven decision-making. However, these approaches also present challenges in terms of data privacy, protection, and governance. In this paper, we investigate the implementation of privacy preservation in the construction of medical Knowledge Base Question Answering (KBQA) based on LLMs. We propose a solution involving fine-grained access control, the de-identification of sensitive data, and prompt engineering with Chain of Thought (CoT). Specifically, the visibility of information is determined based on user identity, and named entity recognition is employed to anonymize personally identifiable information. We also incorporate prompt engineering with Chain of Thought (CoT) to further enhance data privacy. Experimental study has demonstrated the effectiveness of our privacy protection framework.

关键词： Retrieval Augmented Generation Privacy-Preserving LLMs de-identification

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Retrieval-Augmented Code Generation for Universal Information Extraction 13th

Retrieval-Augmented Code Generation for Universal Informatio...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Guo, Yucan Li, Zixuan Jin, Xiaolong Liu, Yantao Zeng, Yutao Liu, Wenxuan Li, Xiang Yang, Pan Bai, Long Guo, Jiafeng Chen, Xueqi Chinese Acad Sci Inst Comp Technol CAS Key Lab Network Data Sci & Technol Beijing Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Information Extraction (IE) aims to extract structural knowledge (e.g., entities, relations, events) from natural language texts. Recently, Large Language Models (LLMs) with code-style prompts have demonstrated powerful capabilities in IE tasks. However, adopting code LLMs to conduct IE tasks still has two challenges: (1) It still lacks a unified code-style prompt for different IE tasks since existing methods use task-specific prompts for separate IE tasks. (2) It still lacks an effective in-context learning (ICL) method to encourage LLMs to conduct IE tasks precisely, considering some powerful LLMs are close-sourced and not trainable. Therefore, this paper proposes a code generation framework for Universal IE (UIE) tasks called Code4UIE. Specifically, for the first challenge, Code4UIE designs a unified code-style schema for various IE tasks via Python classes. By so doing, different IE tasks can be associated, and LLMs can learn from various IE tasks effectively. For the second challenge, Code4UIE adopts a retrieval-augmented mechanism to comprehensively utilize the ICL ability of LLMs. Extensive experiments on five representative IE tasks across nine datasets demonstrate the effectiveness of the Code4UIE framework.

关键词： Information Extraction Code Generation Large Language Model Information Retrieval

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A Comparative Study for Contextualized Spoken Answer Classification in German Medical Questionnaires 26th

A Comparative Study for Contextualized Spoken Answer Classif...

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26th International Conference on Speech and Computer

作者： Harnisch, Philipp L. Schuhmann, Daniel Hillmann, Stefan Tech Univ Berlin Qual & Usabil Lab Berlin Germany

ISBN: (纸本)9783031779602;9783031779619

This paper presents a study aimed at enhancing the classification accuracy of patients' spoken answer selections to German medical Patient-Reported Outcome Measures (PROM) questionnaires within a multimodal dialog system. We collected 1,737 speech data samples for training and evaluation through a lab experiment, employing textual priming as opposed to the visual priming utilized in prior research. For classification, we compare results from utilizing sentence embeddings against results from prompting various Large Language Models. We conduct a comparative analysis of approaches in terms of prediction performance, efficiency, hardware restraints, budget, inference time, and data privacy. Further, we investigate if adding the survey item text as context improves the classification. Results show the highest accuracy for gpt-4 prompting, and indicate that including the questionnaire item text alongside user utterances is beneficial for LLM prompting. Additionally, we find significant positive correlations between accuracy and certain prompt characteristics.

关键词： Text classification Speech-to-text German medical questionnaires Large language models Prompt engineering

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