基于多注意力机制的维吾尔语人称代词指代消解

杨启萌; 禹龙; 田生伟; 艾山·吾买尔

doi:10.16383/j.aas.c180678

基于多注意力机制的维吾尔语人称代词指代消解

doi: 10.16383/j.aas.c180678

杨启萌^{1, 2, 3,},
禹龙^{2, 3, 4, ,},
田生伟^{1, 2, 3,},
艾山·吾买尔^{2, 3, 5,}

1.
新疆大学软件学院乌鲁木齐 830008
2.
新疆大学软件工程技术重点实验室乌鲁木齐 830046
3.
新疆大学信号与信息处理重点实验室乌鲁木齐 830046
4.
新疆大学网络中心乌鲁木齐 830046
5.
新疆大学信息科学与工程学院乌鲁木齐 830046

基金项目:

国家自然科学基金 61563051

国家自然科学基金 61662074

国家自然科学基金 61962057

国家自然科学基金重点项目 U2003208

自治区重大科技项目 2020A03004-4

新疆自治区科技人才培养项目 QN2016YX0051

详细信息

作者简介:
杨启萌  新疆大学博士研究生. 主要研究方向为自然语言处理.E-mail: yqm_xju@163.com

田生伟  新疆大学教授. 主要研究方向为自然语言处理和计算机智能技术.E-mail: tianshengwei@163.com

艾山·吾买尔  新疆大学副教授. 主要研究方向为自然语言处理及机器翻译.E-mail: Hasan1479@xju.edu.cn

通讯作者:
禹龙新疆大学教授. 主要研究方向为计算机智能技术与计算机网络. 本文通信作者. E-mail: yul_xju@163.com

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出版历程
- 收稿日期: 2018-10-18
- 录用日期: 2018-12-24
- 刊出日期: 2021-06-10

Anaphora Resolution of Uyghur Personal Pronouns Based on Multi-attention Mechanism

YANG Qi-Meng^{1, 2, 3
,},
YU Long^{2, 3, 4
, ,},
TIAN Sheng-Wei^{1, 2, 3
,},
AISHAN Wumaier^{2, 3, 5
,}

1.
School of Software, Xinjiang University, Urumqi 830008
2.
Key Laboratory of software engineering technology, Xinjiang University, Urumqi 830046
3.
Key Laboratory of Signal and Information Processing, Xinjiang University, Urumqi 830046
4.
Network Center, Xinjiang University, Urumqi 830046
5.
College of formation Science and Technology, Xinjiang University, Urumqi 830046

Funds:

National Natural Science Foundation of China 61563051

National Natural Science Foundation of China 61662074

National Natural Science Foundation of China 61962057

Key Program of National Natural Science Foundation of China U2003208

Major Science and Technology Projects in the Autonomous Region 2020A03004-4

Xinjiang Uygur Autonomous Region Scientiflc and Technological Personnel Training Project QN2016YX0051

More Information

Author Bio:
YANG Qi-Meng  Ph. D. candidate at Xinjiang University. His main research interest is natural language processing

TIAN Sheng-Wei  Professor at Xinjiang University. His research interest covers natural language processing and computer intelligence technology

AISHAN Wumaier  Associate professor at Xinjiang University. His research interest covers natural language processing and machine translation

Corresponding author: YU Long Professor at Xinjiang University. Her research interest covers computer intelligence technology and computer networks. Corresponding author of this paper

摘要

摘要:
针对深度神经网络模型学习照应语和候选先行语的语义信息忽略了每一个词在句中重要程度, 且无法关注词序列连续性关联和依赖关系等问题, 提出一种结合语境多注意力独立循环神经网络(Contextual multi-attention independently recurrent neural network, CMAIR) 的维吾尔语人称代词指代消解方法. 相比于仅依赖照应语和候选先行语语义信息的深度神经网络, 该方法可以分析上下文语境, 挖掘词序列依赖关系, 提高特征表达能力. 同时, 该方法结合多注意力机制, 关注待消解对多层面语义特征, 弥补了仅依赖内容层面特征的不足, 有效识别人称代词与实体指代关系. 该模型在维吾尔语人称代词指代消解任务中的准确率为90.79 %, 召回率为83.25 %, F值为86.86 %. 实验结果表明, CMAIR模型能显著提升维吾尔语指代消解性能.
- 注意力机制 /
- 语境 /
- 独立循环神经网络 /
- 指代消解
Abstract:
The deep neural network model learns the semantic information of anaphora and candidate antecedent, ignores the importance of each word in the sentence, and cannot pay attention to the continuous association and dependence of the word sequence. This paper proposes a Uyghur personal pronoun anaphora resolution method based on contextual multi-attention independent recurrent neural network (CMAIR). Compared with deep neural networks that rely only on the semantic information of anaphora and candidate antecedent, this method can analyze context relations, mine word sequence dependencies, and improve feature expression ability. At the same time, this method combines the multiattention mechanism, pays attention to the multi-layer semantic features to be resolved, efiectively compensates for the lack of content-level features, and efiectively recognizes the relationship between personal pronouns and entities. The precision rate of this method in the Uyghur personal pronoun anaphora resolution task is 90.79 %, the recall rate is 83.25 %, and the F value is 86.86 %. The experimental results show that the CMAIR model can signiflcantly improve the performance of Uyghur personal pronoun anaphora resolution.
- Attention mechanism /
- context /
- independently recurrent neural network /
- anaphora resolution
Recommended by Associate Editor ZHANG Min
注释:

1) 本文责任编委张民

HTML全文

图 1 维吾尔语人称代词指代消解例句

Fig. 1 The example of Uyghur personal pronoun anaphora resolution

下载: 全尺寸图片幻灯片

图 2 IndRNN结构图

Fig. 2 The structure diagram of IndRNN

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图 3 多注意力机制IndRNN模型框架图

Fig. 3 IndRNN model framework with multiple attention mechanisms

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图 4 距离计算方式举例

Fig. 4 Example of distance calculation

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图 5 不同维度词向量分类F-score比较

Fig. 5 Comparison of difierent dimension word vector classiflcation F-score

下载: 全尺寸图片幻灯片

表 1 词语句中成分标注

Table 1 Component labeling of words in sentences

表 2 词性标注

Table 2 Part of speech tagger

表 3 hand-crafted特征

Table 3 The feature of hand-crafted

照应语词性		词性一致		单复数一致		性别一致			先行语语义角色			照应语语义角色			存在嵌套
人称代词	非人称代词	是	否	是	否	是	否	未知	施事者	受事者	无	施事者	受事者	无	是	否
1	0	1	0	1	0	1	0	0.5	1	0.5	0	1	0.5	0	0	1

下载: 导出CSV

表 4 实验参数设置

Table 4 Hyper parameters of experiment

Parameter	Parameter description	Value
t	Training epochs	50
b	Batch	100
d	Dropout rate	0.5
l	IndRNN layers	3
k	Kernel Size	3

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表 5 与以往研究对比(%)

Table 5 Compared with previous studies (%)

Model	P	R	F
Tian	82.33	72.07	76.86
Li	88	80	83.81
CMAIR	90.79	83.25	86.86

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表 6 不同模型消解性能对比(%)

Table 6 Comparison of different model anaphora resolution performance (%)

Model	P	R	F
CNN	75.47	74.16	74.81
ATT-CNN-1	80.14	77.46	78.78
ATT-CNN-2	82.37	78.80	80.55
ATT-CNN-3	83.02	79.61	81.27

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表 7 不同特征类型对指代消解性能影响(%)

Table 7 The effect of different feature types on the anaphora resolution (%)

特征类型	P	R	F
V_attention + V_context	83.29	79.43	81.31
V_hand-crafted + V_attention	86.81	80.24	83.40
CMAIR	90.79	83.25	86.86

下载: 导出CSV

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