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

共 57438 条记录，以下是11-20 订阅

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Retrieve-or-Copy: Enhancing Chinese Spelling Check with Retrieval and Copy Mechanism 13th

Retrieve-or-Copy: Enhancing Chinese Spelling Check with Retr...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Ding, Hong Liu, Qingyi Wang, Haitao Wu, Hejun Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Recent performance improvement of the Chinese Spelling Check (CSC) has shown the effectiveness of introducing a non-parametric memory component to retrieve similar examples. Nevertheless, most existing approaches only use this memory during testing, which hinders the memory retriever from adapting to the specific CSC model and leads to sub-optimal error correction. Furthermore, these approaches suffer from inefficient inference since they perform memory retrieval on every position throughout an entire sentence. This paper proposes a framework called Retrieve-or-Copy (RoC) to enhance the standard CSC model with an error detector, a learnable retriever, and a character-level copy mechanism. Specifically, in RoC, the error detector identifies incorrect characters before retrieval, enabling the retriever to focus solely on errors. By jointly optimizing the retriever and the CSC model with spelling error correction as the ultimate training objective, the retriever is more likely to find suitable examples for the CSC model to achieve better performance. Additionally, the copy mechanism allows the CSC model to access the correct character in the input to avoid over-correction. Experimental results demonstrate that RoC outperforms methods that only use non-parametric memory during testing and achieves comparable or even better performance than state-of-the-art CSC models on SIGHAN datasets. Our code is publicly available at https://***/dingdingtom/csc_roc.

关键词： Chinese Spelling Check Learnable Retrieval Copy Mechanism

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A Survey of Zero-Shot Stance Detection 13th

A Survey of Zero-Shot Stance Detection

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Liu, Guangzhen Zhao, Kai Zhang, Linlin Bi, Xuehua Lv, Xiaoyi Chen, Cheng Xinjiang Univ Sch Comp Sci & Technol Urumqi 830049 Xinjiang Peoples R China Xinjiang Univ Sch Software Xi Urumqi 830049 Xinjiang Peoples R China Xinjiang Med Univ Coll Med Engn & Technol Urumqi 830017 Xinjiang Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Stance detection is a subproblem of sentiment analysis, commonly defined as classifying the stance of a text toward a certain target: {Favor, Against, Neither}. As an important research problem, reliance on high-quality annotated data poses a significant challenge. However, in the real world, with the rapid development of social media, it is impossible to annotate the massive amount of text on diverse topics, a universal framework for stance detection is expected. Consequently, zero-shot stance detection methods that do not require annotated data have attracted researchers' attention. In this survey, we review and introduce the early research and definition of stance detection, with a focus on summarizing the current research status of ZSSD, discuss datasets and the most advanced models. Finally, based on the above research, we explore possible future directions.

关键词： Natural language processing Deep learning Stance detection Zero-shot

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Improving Automatic Post-editing with Error Prompts Extracted from Quality Estimation 13th

Improving Automatic Post-editing with Error Prompts Extracte...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Xue, Menglong Zhang, Yanliang Henan Polytech Univ Sch Phys & Elect Informat Engn Jiaozuo Henan Peoples R China

ISBN: (纸本)9789819794362;9789819794379

This work introduces a simple yet effective approach to integrate Automatic Post-editing (APE) with Word Level Quality Estimation (QE) using an encoder-decoder Transformer architecture, which we term error-prompted APE. The model employs self-detected translation errors by Word Level QE as prompts to refine translations. Specifically, the error-prompted APE model is self-contained: its encoder functions as a quality estimator, detecting errors in the input translation;the decoder then leverages these identified errors as prompts to refine the translation accordingly. This detailed error prompts enable our model to selectively correct errors while preserving the integrity of the rest of the content. We conduct extensive experiments on English-German and English-Chinese datasets from the WMT shared APE task. The results demonstrate that our proposed approach achieves substantial improvements.

关键词： Automatic Post-editing Quality Estimation Transformer Error Prompts

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MCFC: A Momentum-Driven Clicked Feature Compressed Pre-trained Language Model for Information Retrieval 13th

MCFC: A Momentum-Driven Clicked Feature Compressed Pre-train...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Li, Dongyang Ding, Ruixue Xie, Pengjun He, Xiaofeng East China Normal Univ Shanghai Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9789819794300;9789819794317

Information Retrieval (IR) pre-trained language models are trained from large-scale retrieval-based corpora to promote the task-specific knowledge capacity. Previous works focus on general retrieval pre-trained datasets, which cover inter-document data and intra-document data, paying less attention to the important asset of clicked data which is commonly adopted in recommendation domain. However, the utilization of easily accessible clicked data is a non-trivial operation due to its characteristics of large volume and insufficient refinement, which affect model learning efficiency and imply the risk of distorting learning directions. In this paper, we propose a Momentum-Driven Clicked Feature Compressed Pre-trained Language Models for Information Retrieval (MCFC). Specifically, to tackle the effective learning pace on large amounts of data, we generalize multiple similar feature instances and compress the dispersed knowledge together at the query granularity, named Multi-Instance Information Integration. Meanwhile, more relevant detection between queries and documents is eager in coarse clicked data background, we leverage a momentum-driven adjusting mechanism to refine the text representations, named Continuous Debiasing Calibration. Extensive experiments on downstream datasets validate the superiority of our work to other recent strong baselines.

关键词： Pre-trained Language Model Information Retrieval Clicked Feature

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Overview of the NLPCC 2024 Shared Task 7: Multi-lingual Medical Instructional Video Question Answering 13th

Overview of the NLPCC 2024 Shared Task 7: Multi-lingual Medi...

引用

13th International Conference on Natural Language Processing and Chinese Computing

作者： Li, Bin Weng, Yixuan Song, Qiya Liang, Lianhui Min, Xianwen Zhou, Shoujun Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Chinese Acad Sci Natl Lab Pattern Recognit Inst Automat Beijing Peoples R China Hunan Normal Univ Coll Elect & Informat Engn Changsha Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Following the success of the 1-st Chinese Medical Instructional Video Question Answering (CMIVQA) Challenge in 2023, this year we have hosted the Multilingual Medical Instructional Video Question Answering (MMIVQA) shared task at the NLPCC 2024 conference. The MMIVQA task aims to promote the development of intelligent systems capable of understanding medical instructional video content and accurately delivering visual answers given the questions in a multilingual environment. This task encompasses three challenging tracks: (1) Multilingual Temporal Answer Grounding for Single Video (mTAGSV), (2) Multilingual Video Corpus Retrieval (mVCR), and (3) Multilingual Temporal Answer Grounding for Video Corpus (mTAGVC). These tracks cover different application scenarios ranging from single videos to large-scale video corpora, requiring participants' systems to perform video content understanding, question answering, and temporal localization in both Chinese and English environments. We hope that this new MMIVQA challenge will provide more insights for first aid, medical emergencies, or medical education in multilingual settings.

关键词： Multilingual medical instructional video Multilingual video question answering Multilingual video retrieval Multilingual temporal answer grounding

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Progressive and Consistent Subword Regularization for Neural Machine Translation 13th

Progressive and Consistent Subword Regularization for Neural...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Gao, Yongqi Luo, Yingfeng Zhang, Qinghong Sh, Huibo Xiao, Tong Zhu, Jingbo Northeastern Univ Sch Comp Sci & Engn Shenyang Peoples R China NiuTrans Res Shenyang Peoples R China

ISBN: (纸本)9789819794362;9789819794379

Despite the prevalence of subword tokenization, its deterministic nature-splitting words into unique output tokens, may limit models from fully exploiting the intricate semantic compositions within words. Subword regularization methods address this limitation by using multiple subword sequences generated by tokenization. However, existing methods ineffectively utilize multi-granularity semantic compositions inherent in words, which is crucial for language understanding. In this paper, we propose Progressive and Consistent Subword Regularization (PCSR), a novel and simple subword regularization method that progressively changes the granularity of tokenization from fine to coarse dynamically during training and enforces the consistency between these multi-granularity subword segmentations. Moreover, we verify empirically that applying consistency constraints to existing subword regularization methods significantly improves their effectiveness for neural machine translation (NMT). Experiments on IWSLT and WMT translation tasks show that PCSR outperforms various subword regularization methods and their combinations, with BLEU score improvements up to 2.4 over the standard BPE baseline.

关键词： Subword Regularization Progressive Granularity Neural Machine Translation

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MJP: A Meta-learning Approach for Chinese Legal Judgment Prediction 13th

MJP: A Meta-learning Approach for Chinese Legal Judgment Pre...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Lang, Yuying Hou, Hongxu Chen, Wei Sun, Shuo Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Legal judgment prediction (LJP) is an important task in legal AI that aims to predict outcomes based on the factual description of a case. Most current approaches are data-intensive and focus on introducing external knowledge to improve judgment prediction accuracy. However, they do not address the legal data imbalance problem well, and there is still a large gap between the prediction performance of low-frequency legal events and the overall average performance. Therefore, we propose a method that integrates meta-learning and contrastive learning for Chinese legal judgment prediction (MJP). The approach leverages transferred knowledge from the source category to discern the target category, achieved through constructing support sets and query sets. Additionally, it aligns legal text representations with optimizing metrics and trains the model to effectively distinguish between similar legal factual descriptions. In this way, the model is able to quickly learn effective feature representations from a small amount of labeled data and generalize to new categories. Experimental results show that our proposed approach effectively improves the performance of LJP, not only achieving better than the current model results on two real datasets but also surpassing the performance of other models under low resource settings.

关键词： Legal judgment prediction Meta learning Contrastive learning

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Automatic Detection of Irony Based on Acoustic Features and Facial Expressions 26th

Automatic Detection of Irony Based on Acoustic Features and ...

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26th International Conference on Speech and Computer

作者： Kochetkova, Uliana Slcreline, Pavel Evdokimova, Vera Borisov, Nikolai Scherbakov, Pavel Fedkin, Petr German, Rada St Petersburg State Univ 7-9 Univ Skaya Embankment St Petersburg Russia

ISBN: (纸本)9783031780134;9783031780141

The current study deals with the automatic analysis of verbal irony using artificial neural networks. Detection of verbal irony is an important task nowadays, because the effectiveness of the communication depends on the correct interpretation of sentences with an ambiguous meaning. In the case, when the context is lacking, the correct sense can be understood not from the lexical content, but through phonetic features, as well as through co-speech mimics and gestures. Thus we accomplished a new research on the material of the multimedia corpus of Russian ironic speech, which contains the detailed phonetic annotation and irony evaluation by native listeners in perceptual auditory experiments. Two types of automated analysis were accomplished: based on acoustic feature and facial expression extraction. The use of the fully connected neural network and of the Wav2Vec 2.0 model for the automatic irony detection in audio signal demonstrated high level of irony recognition. We also tested on a part of the corpus the recognition of ironic facial expressions in video signal using convolutional neural network and the PyFeat library, which allowed us to conclude that this model can give good results when we increase the amount of the material.

关键词： Irony Multimedia Speech Corpus artificial Neural Networks Acoustic Feature Extraction Facial Expression Analysis

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Preserving Language Heritage Through Speech Technology: The Case of Upper Sorbian 26th

Preserving Language Heritage Through Speech Technology: The ...

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26th International Conference on Speech and Computer

作者： Kraljevski, Ivan Duckhorn, Frank Sobe, Daniel Tschoepe, Constanze Wolff, Matthias Fraunhofer Inst Ceram Technol & Syst IKTS Dresden Germany Fdn Sorbian People Bautzen Germany Brandenburg Univ Technol BTU Cottbus Senftenberg Chair Commun Engn Cottbus Germany

ISBN: (纸本)9783031779602;9783031779619

The modern world is facing a crisis with the rapid disappearance of endangered languages, which poses a serious threat to global cultural diversity. Speech Technologies and artificial intelligence present promising opportunities to address this crisis by supporting the documentation, revitalization, and everyday use of these vulnerable languages. However, despite recent and remarkable advancements in speech technology, significant challenges persist, particularly for languages with very limited resources and unique linguistic features. This paper details the development of Upper Sorbian speech technologies, focusing on the creation of a practical Speech-to-Text (STT) system as a versatile tool for language preservation. The study explores the current state of Sorbian languages and underscores collaborative efforts with the Foundation for the Sorbian People. Through a series of pilot and successive projects, each phase has contributed to the steady advancement of speech recognition modules and supporting tools, improving their performance, effectiveness and practical usability.

关键词： Endangered languages Speech recognition Upper Sorbian

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A Novel ICD Coding Method Based on Associated and Hierarchical Code Description Distillation 13th

A Novel ICD Coding Method Based on Associated and Hierarchic...

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13th International Conference on Natural Language Processing and Chinese Computing

作者： Thang, Bin Wang, Junli Tongji Univ Minist Educ Key Lab Embedded Syst & Serv Comp Shanghai 201804 Peoples R China Tongji Univ Natl Prov Minist Joint Collaborat Innovat Ctr Fin Shanghai 201804 Peoples R China

ISBN: (纸本)9789819794362;9789819794379

ICD(International Classification of Diseases) coding involves assigning ICD codes to patients visit based on their medical notes. ICD coding is a challenging multilabel text classification problem due to noisy medical document inputs. Recent advancements in automated ICD coding have enhanced performance by integrating additional data and knowledge bases with the encoding of medical notes and codes. However, most of them ignore the code hierarchy, leading to improper code assignments. To address these problems, we propose a novel method based on associated and hierarchical code description distillation (AHDD) for better code representation learning and avoidance of improper code assignment. We utilize the code description and the hierarchical structure inherent to the ICD codes. Therefore, in this paper, we leverage the code description and the hierarchical structure inherent to the ICD codes. The code description is also applied to aware the attention layer and output layer. Experimental results on the benchmark dataset show the superiority of the proposed method over several state-of-the-art baselines.

关键词： ICD coding associated code description hierarchical code description

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