Learning Bidirectional Intent Embeddings by Convolutional Deep Structured Semantic Models for Spoken Language Understanding
Learning Bidirectional Intent Embeddings by Convolutional Deep Structured Semantic Models for Spoken Language Understanding
复制标题
通过卷积深层结构化语义模型学习双向意图嵌入以实现口语理解
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Xiaodong He
中科院分区：
文献类型：
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作者：
Yun;Dilek Z. Hakkani;Xiaodong He
最近智能个人助理的激增激发了对话系统的口语理解。考虑到高级语义，意图嵌入可以被视为通用表示，其帮助导出更灵活的意图模式以克服领域约束和体裁不匹配。应用卷积深度结构化语义模型（CDSSM）来联合学习人类意图和相关联的话语的表示。两组实验，意图扩展和可操作项目检测，进行评估的权力学习的意图嵌入。这些表示桥接可见意图和不可见意图之间的语义关系以用于意图扩展，并且连接来自不同流派的意图以用于可操作项目检测。实验的讨论和分析为减少数据标注的人工努力和消除口语理解的领域和体裁限制提供了未来的方向。
The recent surge of intelligent personal assistants motivates spoken language understanding of dialogue systems. Considering high-level semantics, intent embeddings can be viewed as the universal representations that help derive a more flexible intent schema to overcome the domain constraint and the genre mismatch. A convolutional deep structured semantic model (CDSSM) is applied to jointly learn the representations for human intents and associated utterances. Two sets of experiments, intent expansion and actionable item detection, are conducted to evaluate the power of the learned intent embeddings. The representations bridge the semantic relation between seen and unseen intents for intent expansion, and connect intents from different genres for actionable item detection. The discussion and analysis of experiments provide a future direction for reducing human effort of data annotation and eliminating domain and genre constraints for spoken language understanding.