@inproceedings{mao-etal-2021-ji,
title = "基于数据选择和局部伪标注的跨语义依存分析研究(Selection and Pseudo Partial Annotationy)",
author = "Mao, Dazhan and
Yu, Kuai and
Shao, Yanqiu",
editor = "Li, Sheng and
Sun, Maosong and
Liu, Yang and
Wu, Hua and
Liu, Kang and
Che, Wanxiang and
He, Shizhu and
Rao, Gaoqi",
booktitle = "Proceedings of the 20th Chinese National Conference on Computational Linguistics",
month = aug,
year = "2021",
address = "Huhhot, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2021.ccl-1.59",
pages = "655--666",
abstract = "语义依存分析要走向实用,模型从单领域迁移到其他领域的领域适应能力至关重要。近年来,对抗学习针对领域适应这个任务取得了较好的效果,但对目标领域的无标注数据利用效率并不高。本文采用Self-training这种半监督学习方法,充分发挥无标注数据的潜能,弥补对抗学习方法的不足。但传统的Self-training效率和性能并不好,为此本文针对跨领域语义依存分析这个任务,尝试了强化学习数据选择器,提出了局部伪标注的标注策略,实验结果证明我们提出的模型优于基线模型。",
language = "Chinese",
}
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<abstract>语义依存分析要走向实用,模型从单领域迁移到其他领域的领域适应能力至关重要。近年来,对抗学习针对领域适应这个任务取得了较好的效果,但对目标领域的无标注数据利用效率并不高。本文采用Self-training这种半监督学习方法,充分发挥无标注数据的潜能,弥补对抗学习方法的不足。但传统的Self-training效率和性能并不好,为此本文针对跨领域语义依存分析这个任务,尝试了强化学习数据选择器,提出了局部伪标注的标注策略,实验结果证明我们提出的模型优于基线模型。</abstract>
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%0 Conference Proceedings
%T 基于数据选择和局部伪标注的跨语义依存分析研究(Selection and Pseudo Partial Annotationy)
%A Mao, Dazhan
%A Yu, Kuai
%A Shao, Yanqiu
%Y Li, Sheng
%Y Sun, Maosong
%Y Liu, Yang
%Y Wu, Hua
%Y Liu, Kang
%Y Che, Wanxiang
%Y He, Shizhu
%Y Rao, Gaoqi
%S Proceedings of the 20th Chinese National Conference on Computational Linguistics
%D 2021
%8 August
%I Chinese Information Processing Society of China
%C Huhhot, China
%G Chinese
%F mao-etal-2021-ji
%X 语义依存分析要走向实用,模型从单领域迁移到其他领域的领域适应能力至关重要。近年来,对抗学习针对领域适应这个任务取得了较好的效果,但对目标领域的无标注数据利用效率并不高。本文采用Self-training这种半监督学习方法,充分发挥无标注数据的潜能,弥补对抗学习方法的不足。但传统的Self-training效率和性能并不好,为此本文针对跨领域语义依存分析这个任务,尝试了强化学习数据选择器,提出了局部伪标注的标注策略,实验结果证明我们提出的模型优于基线模型。
%U https://aclanthology.org/2021.ccl-1.59
%P 655-666
Markdown (Informal)
[基于数据选择和局部伪标注的跨语义依存分析研究(Selection and Pseudo Partial Annotationy)](https://aclanthology.org/2021.ccl-1.59) (Mao et al., CCL 2021)
ACL