2024
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基于上下文学习与思维链策略的中文空间语义理解
Wang Shiquan (王士权)
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Fu Weiwei (付薇薇)
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Fang Ruiyu (方瑞玉)
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Li Mengxiang (李孟祥)
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He Zhongjiang (何忠江)
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Li Yongxiang (李永翔)
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Song Shuangyong (宋双永)
Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
“本技术报告详细介绍了我们团队参加第四届中文空间语义理解评测(SpaCE2024)的方法和成果。SpaCE2024旨在全面测试机器对中文空间语义的理解能力,包括空间信息实体识别、空间信息实体识别、空间信息异常识别、空间方位信息推理和空间异形同义识别五个不同的任务。我们团队采用精心设计的prompt并结合微调的方式激发大语言模型的空间语义理解能力,构建了一个高效的空间语义理解系统。在最终的评估中,我们在空间信息实体识别题目中准确率为0.8947,在空间信息实体识别题目中准确率为0.9364,在空间信息异常识别题目中准确率为0.8480,在空间方位信息推理题目中准确率为0.3471,在空间异形同义识别题目中准确率为0.5631,测试集综合准确率为0.6024,排名第一。”
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基于大小模型结合与半监督自训练方法的古文事件抽取
Fu Weiwei (付薇薇)
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Wang Shiquan (王士权)
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Fang Ruiyu (方瑞玉)
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Li Mengxiang (李孟祥)
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He Zhongjiang (何忠江)
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Li Yongxiang (李永翔)
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Song Shuangyong (宋双永)
Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
“本文描述了队伍“TeleAI”在CCL2024古文历史事件类型抽取评测任务(CHED2024)中提交的参赛系统。该任务旨在自动识别出古代文本中的事件触发词与事件类型,其中事件类型判别被分为粗粒度和细粒度的事件类型判别两部分。为了提高古文历史事件类型抽取的性能,我们结合了大模型和小模型,并采用了半监督自训练的方法。在最终的评估中,我们在触发词识别任务得分0.763,粗粒度事件类型判别任务得分0.842,细粒度事件类型判别任务得分0.779,综合得分0.791,在所有单项任务和综合评分上均排名第一。”
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Dual Prompt Tuning based Contrastive Learning for Hierarchical Text Classification
Sishi Xiong
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Yu Zhao
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Jie Zhang
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Li Mengxiang
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Zhongjiang He
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Xuelong Li
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Shuangyong Song
Findings of the Association for Computational Linguistics: ACL 2024
Hierarchical text classification aims at categorizing texts into a multi-tiered tree-structured hierarchy of labels. Existing methods pay more attention to capture hierarchy-aware text feature by exploiting explicit parent-child relationships, while interactions between peer labels are rarely taken into account, resulting in severe label confusion within each layer. In this work, we propose a novel Dual Prompt Tuning (DPT) method, which emphasizes identifying discrimination among peer labels by performing contrastive learning on each hierarchical layer. We design an innovative hand-crafted prompt containing slots for both positive and negative label predictions to cooperate with contrastive learning. In addition, we introduce a label hierarchy self-sensing auxiliary task to ensure cross-layer label consistency. Extensive experiments demonstrate that DPT achieves significant improvements and outperforms the current state-of-the-art methods on BGC and RCV1-V2 benchmark datasets.
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TeleChat: An Open-source Billingual Large Language Model
Zihan Wang
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Liuxz2@chinatelecom.cn Liuxz2@chinatelecom.cn
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Liusx14@chinatelecom.cn Liusx14@chinatelecom.cn
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Yitong Yao
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Huangyy121@chinatelecom.cn Huangyy121@chinatelecom.cn
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Li Mengxiang
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Zhongjiang He
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Liyx25@chinatelecom.cn Liyx25@chinatelecom.cn
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Pulw@chinatelecom.cn Pulw@chinatelecom.cn
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Xuhn@chinatelecom.cn Xuhn@chinatelecom.cn
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Chao Wang
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Shuangyong Song
Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10)
In this paper, we present TeleChat, a collection of large language models (LLMs) with parameters of 7 billion and 12 billion. TeleChat is initially pretrained on an extensive corpus containing a diverse collection of texts from both English and Chinese languages, encompassing trillions of tokens. Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe. We evaluate the performance of TeleChat on various tasks, including general dialogue generation, language understanding, mathematics, reasoning, code generation, and knowledge-based question answering. Our findings indicate that TeleChat achieves state-of-the-art performance to other open-source models of similar size across a wide range of public benchmarks. To support future research and applications utilizing LLMs, we release the fine-tuned model checkpoints of TeleChat-7B and TeleChat-12B, along with code and a portion of our filtered high-quality pretraining data, to the public community.