面向法语口语理解MEDIA基准的新语义任务
New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark
AI总结：
本文针对法语MEDIA数据集缺少意图标注的问题，提出半自动方法构建带意图标注的增强版本，并用联合模型开展意图分类与槽填充实验，扩展了该基准的适用任务范围。
摘要：
意图分类和槽填充是口语理解（SLU）的核心任务。在大多数SLU系统中，这些任务由相互独立的模块实现。约十五年来，人们提出了能够联合完成这两项任务并利用其相互增强作用的模型。欧洲项目HumanE-AI-Net设想使用一个采用联合模型的多语言模块来创建旅游对话系统，并建议组合多个数据集（包括MEDIA数据集）来训练该联合模型。MEDIA SLU数据集是一个法语数据集，自2005年起由ELRA分发，主要由法国研究界使用，自2020年起对学术研究免费开放。遗憾的是，该数据集仅标注了槽位而未标注意图。为此，构建了一个带有意图标注的MEDIA增强版本，以将其用途扩展到更多任务和用例。本文介绍了获得该增强版本所用的半自动方法。此外，我们还展示了在该增强数据集上使用联合模型进行意图分类和槽填充的SLU实验的首批结果。
英文摘要：
Intent classification and slot-filling are essential tasks of Spoken Language Understanding (SLU). In most SLUsystems, those tasks are realized by independent modules. For about fifteen years, models achieving both of themjointly and exploiting their mutual enhancement have been proposed. A multilingual module using a joint modelwas envisioned to create a touristic dialogue system for a European project, HumanE-AI-Net. A combination ofmultiple datasets, including the MEDIA dataset, was suggested for training this joint model. The MEDIA SLU datasetis a French dataset distributed since 2005 by ELRA, mainly used by the French research community and free foracademic research since 2020. Unfortunately, it is annotated only in slots but not intents. An enhanced version ofMEDIA annotated with intents has been built to extend its use to more tasks and use cases. This paper presents thesemi-automatic methodology used to obtain this enhanced version. In addition, we present the first results of SLUexperiments on this enhanced dataset using joint models for intent classification and slot-filling.
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。
