@inproceedings{he-etal-2023-ccl23,
title = "{CCL}23-Eval 任务9系统报告:基于重叠片段生成增强阅读理解模型鲁棒性的方法(System Report for {CCL}23-Eval Task 9: Improving {MRC} Robustness with Overlapping Segments Generation for {GCRC}{\_}adv{R}obust)",
author = "He, Suzhe and
Yang, Chongsheng and
Shi, Shumin",
editor = "Sun, Maosong and
Qin, Bing and
Qiu, Xipeng and
Jiang, Jing and
Han, Xianpei",
booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)",
month = aug,
year = "2023",
address = "Harbin, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2023.ccl-3.32",
pages = "293--302",
abstract = "{``}目前机器阅读理解在抽取语义完整的选项证据时存在诸多挑战。现有通过无监督方式进行证据抽取的工作主要分为两类,一是利用静态词向量,采用集束搜索迭代地提取相关句子;另一类是使用实例级监督方法,包括独立式证据抽取和端到端式证据抽取。前者处理流程上较为繁琐,后者在联合训练时存在不稳定性,直接导致模型性能难以稳定提升。在CCL23-Eval 任务9中,本文提出了一种基于重叠片段生成的自适应端到端证据抽取方法。该方法针对证据句边界不明确的问题,通过将文档划分为多个重叠的句子片段,并提取关键部分作为证据来实现整体语义的抽取。同时,将证据提取嵌入模块予以优化,实现了证据片段置信度自动调整。实验结果表明本文所提出方法能够极大地排除冗余内容干扰,仅需一个超参数即可稳定提升阅读理解模型性能,增强了模型鲁棒性。{''}",
language = "Chinese",
}
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<abstract>“目前机器阅读理解在抽取语义完整的选项证据时存在诸多挑战。现有通过无监督方式进行证据抽取的工作主要分为两类,一是利用静态词向量,采用集束搜索迭代地提取相关句子;另一类是使用实例级监督方法,包括独立式证据抽取和端到端式证据抽取。前者处理流程上较为繁琐,后者在联合训练时存在不稳定性,直接导致模型性能难以稳定提升。在CCL23-Eval 任务9中,本文提出了一种基于重叠片段生成的自适应端到端证据抽取方法。该方法针对证据句边界不明确的问题,通过将文档划分为多个重叠的句子片段,并提取关键部分作为证据来实现整体语义的抽取。同时,将证据提取嵌入模块予以优化,实现了证据片段置信度自动调整。实验结果表明本文所提出方法能够极大地排除冗余内容干扰,仅需一个超参数即可稳定提升阅读理解模型性能,增强了模型鲁棒性。”</abstract>
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%0 Conference Proceedings
%T CCL23-Eval 任务9系统报告:基于重叠片段生成增强阅读理解模型鲁棒性的方法(System Report for CCL23-Eval Task 9: Improving MRC Robustness with Overlapping Segments Generation for GCRC_advRobust)
%A He, Suzhe
%A Yang, Chongsheng
%A Shi, Shumin
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G Chinese
%F he-etal-2023-ccl23
%X “目前机器阅读理解在抽取语义完整的选项证据时存在诸多挑战。现有通过无监督方式进行证据抽取的工作主要分为两类,一是利用静态词向量,采用集束搜索迭代地提取相关句子;另一类是使用实例级监督方法,包括独立式证据抽取和端到端式证据抽取。前者处理流程上较为繁琐,后者在联合训练时存在不稳定性,直接导致模型性能难以稳定提升。在CCL23-Eval 任务9中,本文提出了一种基于重叠片段生成的自适应端到端证据抽取方法。该方法针对证据句边界不明确的问题,通过将文档划分为多个重叠的句子片段,并提取关键部分作为证据来实现整体语义的抽取。同时,将证据提取嵌入模块予以优化,实现了证据片段置信度自动调整。实验结果表明本文所提出方法能够极大地排除冗余内容干扰,仅需一个超参数即可稳定提升阅读理解模型性能,增强了模型鲁棒性。”
%U https://aclanthology.org/2023.ccl-3.32
%P 293-302
Markdown (Informal)
[CCL23-Eval 任务9系统报告:基于重叠片段生成增强阅读理解模型鲁棒性的方法(System Report for CCL23-Eval Task 9: Improving MRC Robustness with Overlapping Segments Generation for GCRC_advRobust)](https://aclanthology.org/2023.ccl-3.32) (He et al., CCL 2023)
ACL