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1568 软件学报 2025 年第 36 卷第 4 期

会对不同槽实体的位置进行区分. 而且 artist、music_item 均属于领域共享槽类型, 显然模型可以已经从源域中获
得这方面的知识. 经过统计, 当数据中存在 artist 时有 38% 的概率存在 music_item, 有 58% 的概率存在 playlist_
owner. artist 与两者有很强的依赖关系, 我们的模型会按照潜在的语言习惯选择合适的答案片段, 而不像 baseline
在预测错误后直接影响另一个强相关的槽类型.

4 总 结

本文提出了基于槽依赖建模的跨领域槽填充方法, 该方法通过生成式的框架同时对多个槽类型进行预测, 并
且每个槽类型的提示序列包含槽语义提示和槽共享提示两部分, 从而引入了槽语义知识和不同槽之间的隐式依
赖, 不同槽类型之间可以互相辅助生成. 对于多实体生成带来的实体类型匹配问题, 我们设计了话语填充子任务来
弥补这一缺陷. 通过填充掩码后的话语, 增强了实体与话语的语义感知, 进而提升了类型匹配的准确率. 在 SNIPS
和 TOP 上的实验结果证实了本文模型在各个方面的性能优势, 并且通过各种比较实验和示例分析验证了模型各
部分的有效性. 在后续的研究中, 我们会进一步挖掘任务中存在的其他依赖或潜在信息, 尽可能从多个角度为模型
提供信息.

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