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检索条件"丛书名=Lecture notes in artificial intelligence,"
57438 条 记 录,以下是161-170 订阅
排序:
FineCSDA: Boosting Document-Level Event Argument Extraction with Fine-Grained Data Augmentation  13th
FineCSDA: Boosting Document-Level Event Argument Extraction ...
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13th International Conference on Natural Language Processing and Chinese Computing
作者: Chen, Junxiu Long, Kehan Li, Shasha Tang, Jintao Wang, Ting Natl Univ Def Technol Coll Comp Sci & Technol Changsha 410073 Peoples R China
Document-level event argument extraction (EAE) aims to extract structured event information from a document. In this paper, we first identify the issue of the long-tail distribution of argument roles in document-level...
来源: 评论
MultiAICL: Multi-task Tuning for Augmented In-Context Learning in Text Style Transfer  13th
MultiAICL: Multi-task Tuning for Augmented In-Context Learni...
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13th International Conference on Natural Language Processing and Chinese Computing
作者: Zhu, Linan Zhou, Zehai Chen, Xiangfan Guo, Xiaolei Kong, Xiangjie Zhejiang Univ Technol Hangzhou Zhejiang Peoples R China
In-context learning (ICL) enhances the performance of large language models (LLMs) across various natural language process (NLP) tasks by simply demonstrating a few-shot of examples or instructions during inference. H...
来源: 评论
Emotion Cause Extraction in Conversations with Response Graphing  13th
Emotion Cause Extraction in Conversations with Response Grap...
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13th International Conference on Natural Language Processing and Chinese Computing
作者: Tian, Yuanhe Cheng, Pengsen Xia, Fei Liu, Jiayong Zhang, Yongdong Song, Yan Univ Washington Seattle WA 98195 USA Univ Sci & Technol China Hefei Peoples R China Sichuan Univ Chengdu Peoples R China
Emotion cause extraction in conversations (ECEC) is an important task in emotion analysis, aiming to extract the text spans, i.e., parts in utterances, that reflect the causes of a certain type of emotion embedded in ...
来源: 评论
Autogenerated MQM Data for Quality Estimation Based on Sequence Labeling  13th
Autogenerated MQM Data for Quality Estimation Based on Seque...
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13th International Conference on Natural Language Processing and Chinese Computing
作者: Ge, Wei Lei, Tao Gong, Zhengxian Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China
Quality Estimation (QE) for machine translation aims to evaluate translation quality without reference translations. The WMT 2022 competition introduced a novel QE task focused on predicting MQM labels, i.e. human ann...
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Evaluating the Fidelity of Image Captioning via Weighted Boolean Question Answering  13th
Evaluating the Fidelity of Image Captioning via Weighted Boo...
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13th International Conference on Natural Language Processing and Chinese Computing
作者: Wang, Kaixuan Li, Shasha Tang, Jintao Long, Kehan Miao, Yongzhu Chen, Fangda Wang, Ting Natl Univ Def Technol Coll Comp Sci & Technol Changsha 410073 Peoples R China
Image captioning evaluation is of great significance in guiding caption generation, practically valuable evaluation requires fine-grained evaluation metrics. However, most of the current metrics are only capable of me...
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Generation of Scientific Literature Surveys Based on Large Language Models (LLM) and Multi-Agent Systems (MAS)  13th
Generation of Scientific Literature Surveys Based on Large L...
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13th International Conference on Natural Language Processing and Chinese Computing
作者: Qi, Ruihua Li, Weilong Lyu, Haobo Dalian Univ Foreign Languages Sch Software Engn Dalian 116044 Peoples R China Dalian Univ Foreign Languages Res Ctr Language Intelligence Dalian 116044 Peoples R China
With the rapid increase in the number and speed of scientific publications, researchers face significant time pressure when conducting literature reviews. This paper presents an automatic literature review generation ...
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DDR-ECC: Dictionary-Driven Chinese ASR Entity Correction with Controllable Decoding  13th
DDR-ECC: Dictionary-Driven Chinese ASR Entity Correction wit...
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13th International Conference on Natural Language Processing and Chinese Computing
作者: Wang, Dejun Peking Univ Beijing Peoples R China
ASR error correction is an effective method for optimizing ASR recognition results. The current mainstream ASR error correction system is mainly based on the encoder-decoder structure, by learning the mapping of incor...
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LaiDA: Linguistics-Aware In-Context Learning with Data Augmentation for Metaphor Components Identification  13th
LaiDA: Linguistics-Aware In-Context Learning with Data Augme...
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13th International Conference on Natural Language Processing and Chinese Computing
作者: Liu, Hongde He, Chenyuan Meng, Feiyang Niu, Changyong Jia, Yuxiang Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou Peoples R China
Metaphor Components Identification (MCI) contributes to enhancing machine understanding of metaphors, thereby advancing downstream natural language processing tasks. However, the complexity, diversity, and dependency ...
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Topic-Awared Contrastive Learning for Incoming Fake News Detection in News Streams  13th
Topic-Awared Contrastive Learning for Incoming Fake News Det...
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13th International Conference on Natural Language Processing and Chinese Computing
作者: Zhang, Yongcheng Xiang, Changpeng Ren, Kai Wei, Xiaomei Huazhong Agr Univ Coll Informat Wuhan Peoples R China South Cent Minzu Univ Coll Comp Sci Wuhan Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Agr Wuhan Peoples R China
The prevalence of social media streams has transformed them into essential sources of real-time information on various topics. However, the simultaneous spread of fake news poses a significant challenge to the authent...
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RAG and Few-Shot Prompting in Emotional Text Generation  26th
RAG and Few-Shot Prompting in Emotional Text Generation
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26th International Conference on Speech and Computer
作者: Vologina, Elizaveta Matveeva, Anastasiia Makhnytka, Olesia Matveev, Yuri Burambayeva, Nursaule ITMO Univ St Petersburg 197101 Russia LN Gumilyov Eurasian Natl Univ Astana 010000 Kazakhstan
This scientific article describes a modification of the Retrieval Augmented Generation (RAG) method aimed at increasing the emotional content of model responses without supervised fine-tuning. In this model, RAG is no...
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