PerSHOP——用于购物对话系统建模的波斯语数据集
PerSHOP -- A Persian dataset for shopping dialogue systems modeling
AI总结：
本文构建了目前最大的波斯语购物对话数据集PerSHOP，包含15个领域、1061个对话及近22k条话语，并提出执行意图分类与实体提取的NLU基线模型，F-1分数分别约为91%和93%。
摘要：
如今，对话系统被广泛应用于工业和研究的诸多领域。这些系统已有许多成功的实例，如Apple Siri、Google Assistant和IBM Watson。任务型对话系统是其中的一类，用于处理特定任务。它们可以执行诸如预订机票或预订餐厅等任务。购物是这些系统中最受欢迎的领域之一。机器人取代了人工销售员，通过语音与顾客进行交互。为了训练这些系统背后的模型，需要带有标注的数据。在本文中，我们通过众包方式开发了一个波斯语对话数据集。我们对这些对话进行了标注以训练模型。该数据集包含分布在15个不同领域和1061个对话中的近22k条话语。这是该领域最大的波斯语数据集，被免费提供以便未来的研究人员使用。此外，我们还提出了一些用于自然语言理解（NLU）任务的基线模型。这些模型执行NLU的两项任务：意图分类和实体提取。意图分类获得的F-1分数约为91%，实体提取约为93%，这可以作为未来研究的基线。
英文摘要：
Nowadays, dialogue systems are used in many fields of industry and research. There are successful instances of these systems, such as Apple Siri, Google Assistant, and IBM Watson. Task-oriented dialogue system is a category of these, that are used in specific tasks. They can perform tasks such as booking plane tickets or making restaurant reservations. Shopping is one of the most popular areas on these systems. The bot replaces the human salesperson and interacts with the customers by speaking. To train the models behind the scenes of these systems, annotated data is needed. In this paper, we developed a dataset of dialogues in the Persian language through crowd-sourcing. We annotated these dialogues to train a model. This dataset contains nearly 22k utterances in 15 different domains and 1061 dialogues. This is the largest Persian dataset in this field, which is provided freely so that future researchers can use it. Also, we proposed some baseline models for natural language understanding (NLU) tasks. These models perform two tasks for NLU: intent classification and entity extraction. The F-1 score metric obtained for intent classification is around 91% and for entity extraction is around 93%, which can be a baseline for future research.
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。
