面向零样本框架语义解析的任务无关本体与简单标签
Towards Zero-Shot Frame Semantic Parsing with Task Agnostic Ontologies and Simple Labels
AI总结：
提出OpenFSP框架，利用领域无关槽位类型和简单标签，结合句子编码器匹配算法，实现零样本框架语义解析，在TopV2数据集上优于强基线。
摘要：
框架语义解析是任务导向型对话系统的重要组成部分。当前模型依赖大量训练数据来成功识别用户输入话语中的意图和槽位。这为虚拟助手能力添加新领域设置了重大障碍，因为创建这些数据需要高度专业化的自然语言处理专业知识。在这项工作中，我们提出了OpenFSP，一个允许从少量无需特定NLP知识即可生成的简单标签中轻松创建新领域的框架。我们的方法依赖于创建一组小而富有表现力的领域无关槽位类型，使得新领域的标注变得容易。给定这样的标注，一个依赖句子编码器的匹配算法预测由最终用户定义的领域的意图和槽位。在TopV2数据集上的大量实验表明，我们的模型在这种简单标签设置中优于强基线。
英文摘要：
Frame semantic parsing is an important component of task-oriented dialogue systems. Current models rely on a significant amount training data to successfully identify the intent and slots in the user's input utterance. This creates a significant barrier for adding new domains to virtual assistant capabilities, as creation of this data requires highly specialized NLP expertise. In this work we propose OpenFSP, a framework that allows for easy creation of new domains from a handful of simple labels that can be generated without specific NLP knowledge. Our approach relies on creating a small, but expressive, set of domain agnostic slot types that enables easy annotation of new domains. Given such annotation, a matching algorithm relying on sentence encoders predicts the intent and slots for domains defined by end-users. Extensive experiments on the TopV2 dataset shows that our model outperforms strong baselines in this simple labels setting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
