Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (3): 715-721.DOI: 10.11772/j.issn.1001-9081.2023030358
Special Issue: 人工智能
• Artificial intelligence • Previous Articles Next Articles
Zongze JIA, Pengfei GAO, Yinglong MA(), Xiaofeng LIU, Haixin XIA
Received:
2023-04-04
Revised:
2023-06-06
Accepted:
2023-06-08
Online:
2023-07-04
Published:
2024-03-10
Contact:
Yinglong MA
About author:
JIA Zongze, born in 1997, M. S. candidate. His research interests include natural language processing.Supported by:
通讯作者:
马应龙
作者简介:
贾宗泽(1997—),男,山西运城人,硕士研究生,主要研究方向:自然语言处理基金资助:
CLC Number:
Zongze JIA, Pengfei GAO, Yinglong MA, Xiaofeng LIU, Haixin XIA. Multi-feature fusion attention-based hierarchical classification method for dialogue act[J]. Journal of Computer Applications, 2024, 44(3): 715-721.
贾宗泽, 高鹏飞, 马应龙, 刘晓峰, 夏海鑫. 基于注意力机制的多特征融合对话行为层次化分类方法[J]. 《计算机应用》唯一官方网站, 2024, 44(3): 715-721.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023030358
数据集 | 训练集 | 验证集 | 测试集 | |||||
---|---|---|---|---|---|---|---|---|
对话数 | 话语数/103 | 对话数 | 话语数/103 | 对话数 | 话语数/103 | |||
MRDA | 5 | 10 | 51 | 76 | 11 | 15 | 11 | 15 |
SwDA | 42 | 19 | 1 003 | 173 | 112 | 22 | 19 | 4 |
Tab. 1 Information of experiment datasets
数据集 | 训练集 | 验证集 | 测试集 | |||||
---|---|---|---|---|---|---|---|---|
对话数 | 话语数/103 | 对话数 | 话语数/103 | 对话数 | 话语数/103 | |||
MRDA | 5 | 10 | 51 | 76 | 11 | 15 | 11 | 15 |
SwDA | 42 | 19 | 1 003 | 173 | 112 | 22 | 19 | 4 |
类型 | 模型 | MRDA | SwDA | ||
---|---|---|---|---|---|
Acc | F1 | Acc | F1 | ||
扁平分类器 | CNN-prosody | 84.7 | 79.3 | 75.1 | 70.6 |
STM | 91.4 | 87.1 | 83.2 | 79.1 | |
SPARTA | 90.2 | 85.9 | 80.1 | 76.2 | |
MDOM | 91.9 | 87.6 | 81.6 | 77.8 | |
DARER | 93.2 | 88.6 | 83.9 | 79.2 | |
层次分类器 | Bi-LSTM-CRF | 90.9 | 85.6 | 79.2 | 74.3 |
NSIM | 89.9 | 85.1 | 80.5 | 76.1 | |
BiRNN-attention | 91.1 | 87.9 | 82.9 | 79.4 | |
Dual-attention | 92.2 | 88.1 | 82.3 | 78.6 | |
HLSN | 90.5 | 86.3 | 82.9 | 76.9 | |
UIIM | 89.9 | 85.7 | 78.6 | 74.8 | |
本文方法 | MFA-HC | 93.3 | 88.5 | 84.4 | 79.5 |
Tab. 2 Result comparison of different models on SwDA and MRDA
类型 | 模型 | MRDA | SwDA | ||
---|---|---|---|---|---|
Acc | F1 | Acc | F1 | ||
扁平分类器 | CNN-prosody | 84.7 | 79.3 | 75.1 | 70.6 |
STM | 91.4 | 87.1 | 83.2 | 79.1 | |
SPARTA | 90.2 | 85.9 | 80.1 | 76.2 | |
MDOM | 91.9 | 87.6 | 81.6 | 77.8 | |
DARER | 93.2 | 88.6 | 83.9 | 79.2 | |
层次分类器 | Bi-LSTM-CRF | 90.9 | 85.6 | 79.2 | 74.3 |
NSIM | 89.9 | 85.1 | 80.5 | 76.1 | |
BiRNN-attention | 91.1 | 87.9 | 82.9 | 79.4 | |
Dual-attention | 92.2 | 88.1 | 82.3 | 78.6 | |
HLSN | 90.5 | 86.3 | 82.9 | 76.9 | |
UIIM | 89.9 | 85.7 | 78.6 | 74.8 | |
本文方法 | MFA-HC | 93.3 | 88.5 | 84.4 | 79.5 |
类型 | 模型 | 第1层 | 第2层 | 第3层 |
---|---|---|---|---|
层次分类器 | Bi-LSTM-CRF | 90.9 | 86.1 | 79.2 |
NSIM | 92.1 | 87.4 | 80.5 | |
BiRNN-attention | 94.4 | 89.3 | 82.9 | |
Dual-attention | 94.7 | 89.1 | 82.3 | |
HLSN | 93.9 | 88.8 | 81.9 | |
本文方法 | MFA-HC | 94.6 | 90.1 | 84.4 |
Tab. 3 Accuracies of MFA-HC and other hierarchical classification models on SwDA
类型 | 模型 | 第1层 | 第2层 | 第3层 |
---|---|---|---|---|
层次分类器 | Bi-LSTM-CRF | 90.9 | 86.1 | 79.2 |
NSIM | 92.1 | 87.4 | 80.5 | |
BiRNN-attention | 94.4 | 89.3 | 82.9 | |
Dual-attention | 94.7 | 89.1 | 82.3 | |
HLSN | 93.9 | 88.8 | 81.9 | |
本文方法 | MFA-HC | 94.6 | 90.1 | 84.4 |
间隔大小 | 不同数据集下的Acc/% | |
---|---|---|
MRDA | SwDA | |
2 | 91.6 | 81.9 |
3 | 92.2 | 83.1 |
4 | 92.9 | 83.9 |
5 | 93.3 | 84.4 |
6 | 92.7 | 83.7 |
7 | 91.4 | 82.6 |
Tab. 4 Accuracy comparison with different intervals of utterance length classification on SwDA and MRDA
间隔大小 | 不同数据集下的Acc/% | |
---|---|---|
MRDA | SwDA | |
2 | 91.6 | 81.9 |
3 | 92.2 | 83.1 |
4 | 92.9 | 83.9 |
5 | 93.3 | 84.4 |
6 | 92.7 | 83.7 |
7 | 91.4 | 82.6 |
类型 | 模型 | MRDA | SwDA |
---|---|---|---|
特征 | w/o词性特征 | 90.9 | 82.6 |
w/o语言学统计特征 | 92.2 | 83.9 | |
w/o词性特征&语言学统计特征 | 88.3 | 81.2 | |
融合 | w/o共性 | 91.0 | 82.0 |
w/o个性 | 90.7 | 82.1 | |
w/o UIM | 88.2 | 80.3 | |
MFA-HC | 93.3 | 84.4 |
Tab. 5 Results of ablation study on different test sets (Accuarcy)
类型 | 模型 | MRDA | SwDA |
---|---|---|---|
特征 | w/o词性特征 | 90.9 | 82.6 |
w/o语言学统计特征 | 92.2 | 83.9 | |
w/o词性特征&语言学统计特征 | 88.3 | 81.2 | |
融合 | w/o共性 | 91.0 | 82.0 |
w/o个性 | 90.7 | 82.1 | |
w/o UIM | 88.2 | 80.3 | |
MFA-HC | 93.3 | 84.4 |
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