强大的开放意图分类在多镜头和少数镜头场景
Jingkai Wang1, Xiangkun Wang1, Jiafen Liu1
1School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, 611130, China.
概括
强大的开放意图分类 (ROIC) 通过准确分类已知的和未知的意图来改进对话系统. 这种新的方法提高了决策边界,无论是多次射击还是少次射击的场景,提高了整体绩效.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 开放意图分类 (OIC) 对于对话系统处理已知和新用户意图至关重要.
- 目前使用BERT微调的OIC方法面临着由于固定的特征尺寸而在多镜头和少镜头场景中不足/过度装配的挑战.
- 有效的数据分布建模和精确的决策界限定义仍然是OIC的重大挑战.
研究的目的:
- 提出一个强大的开放意图分类 (ROIC) 方法,在多次拍摄和少次拍摄设置中有效.
- 增强数据分布的建模,提高OIC决策边界的精度.
- 解决现有的OIC方法在处理各种意图场景方面的局限性.
主要方法:
- 通过将对距离升级为完整的距离矩阵来实现远距离意识的对比学习,以捕捉全球关系.
- 采用硬负采样采矿,以增强类间分离性和类内聚合性.
- 引入了功能空间转换,将表示映射到适合强大的决策边界构建的空间中.
主要成果:
- 拟议的ROIC方法在多次射击和少数次射击场景中都表现出显著的有效性.
- ROIC成功地改进了数据分布的建模和决策边界的定义.
- 标准基准的实验结果验证了ROIC方法的稳定性和有效性.
结论:
- ROIC为开放意图分类提供了一个强大的解决方案,在多次射击和少次射击学习中表现优于现有的方法.
- 远程意识的对比学习和特征空间转换的结合有效地解决了OIC的挑战.
- 这项研究有助于更准确,更可靠的对话系统,能够处理更广泛的用户意图.
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