@inproceedings{zhu-etal-2021-jie,
title = "结合边界预测和动态模板方法的槽填充模型(Slot Filling Model with Boundary Prediction and Dynamic Template)",
author = "Zhu, Zhanbiao and
Huang, Peijie and
Zhang, Yexing and
Liu, Shudong and
Zhang, Hualin and
Huang, Junyao",
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-1.32/",
pages = "339--349",
language = "zho",
abstract = "意图识别和槽信息填充的联合模型将口语理解技术(Spoken language understandingSLU)提升到了一个新的水平,但是目前研究进展的模型通过话语上下文信息判断位置信息,缺少对槽信息标签之间位置信息的考虑,导致模型在槽位提取过程中容易发生边界错误,进而影响最终槽位提取表现。而且在槽信息提取任务中,槽指称项(Slot mentions)可能与正常表述话语并没有区别,特别是电影名字、歌曲名字等,模型容易受到槽指称项话语的干扰,因而无法在槽位提取中正确识别槽位边界。本文提出了一种面向口语理解的结合边界预测和动态模板的槽填充(Boundary-predictionand Dynamic-template Slot Filling BDSF)模型。该模型提供了一种联合预测边界信息的辅助任务,将位置信息引入到槽信息填充中,同时利用动态模版机制对话语句式建模,能够让模型聚焦于话语中的非槽指称项部分,避免了模型被槽指称项干扰,增强模型区分槽位边界的能力。在公共基准语料库CAIS和SMP-ECDT上的实验结果表明,我们的模型优于比较模型,特别是能够为槽标签预测模型提供准确的位置信息。"
}
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<abstract>意图识别和槽信息填充的联合模型将口语理解技术(Spoken language understandingSLU)提升到了一个新的水平,但是目前研究进展的模型通过话语上下文信息判断位置信息,缺少对槽信息标签之间位置信息的考虑,导致模型在槽位提取过程中容易发生边界错误,进而影响最终槽位提取表现。而且在槽信息提取任务中,槽指称项(Slot mentions)可能与正常表述话语并没有区别,特别是电影名字、歌曲名字等,模型容易受到槽指称项话语的干扰,因而无法在槽位提取中正确识别槽位边界。本文提出了一种面向口语理解的结合边界预测和动态模板的槽填充(Boundary-predictionand Dynamic-template Slot Filling BDSF)模型。该模型提供了一种联合预测边界信息的辅助任务,将位置信息引入到槽信息填充中,同时利用动态模版机制对话语句式建模,能够让模型聚焦于话语中的非槽指称项部分,避免了模型被槽指称项干扰,增强模型区分槽位边界的能力。在公共基准语料库CAIS和SMP-ECDT上的实验结果表明,我们的模型优于比较模型,特别是能够为槽标签预测模型提供准确的位置信息。</abstract>
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%0 Conference Proceedings
%T 结合边界预测和动态模板方法的槽填充模型(Slot Filling Model with Boundary Prediction and Dynamic Template)
%A Zhu, Zhanbiao
%A Huang, Peijie
%A Zhang, Yexing
%A Liu, Shudong
%A Zhang, Hualin
%A Huang, Junyao
%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 zho
%F zhu-etal-2021-jie
%X 意图识别和槽信息填充的联合模型将口语理解技术(Spoken language understandingSLU)提升到了一个新的水平,但是目前研究进展的模型通过话语上下文信息判断位置信息,缺少对槽信息标签之间位置信息的考虑,导致模型在槽位提取过程中容易发生边界错误,进而影响最终槽位提取表现。而且在槽信息提取任务中,槽指称项(Slot mentions)可能与正常表述话语并没有区别,特别是电影名字、歌曲名字等,模型容易受到槽指称项话语的干扰,因而无法在槽位提取中正确识别槽位边界。本文提出了一种面向口语理解的结合边界预测和动态模板的槽填充(Boundary-predictionand Dynamic-template Slot Filling BDSF)模型。该模型提供了一种联合预测边界信息的辅助任务,将位置信息引入到槽信息填充中,同时利用动态模版机制对话语句式建模,能够让模型聚焦于话语中的非槽指称项部分,避免了模型被槽指称项干扰,增强模型区分槽位边界的能力。在公共基准语料库CAIS和SMP-ECDT上的实验结果表明,我们的模型优于比较模型,特别是能够为槽标签预测模型提供准确的位置信息。
%U https://aclanthology.org/2021.ccl-1.32/
%P 339-349
Markdown (Informal)
[结合边界预测和动态模板方法的槽填充模型(Slot Filling Model with Boundary Prediction and Dynamic Template)](https://aclanthology.org/2021.ccl-1.32/) (Zhu et al., CCL 2021)
ACL