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.

本文通过众包开发了包含近 22k 个发言、15 个不同领域和 1061 个对话的波斯语对话数据集，并对其进行了标注以训练模型。同时，我们提出了一些用于自然语言理解（NLU）任务的基准模型，其中，意图分类的 F-1 得分约为 91％，实体抽取的 F-1 得分约为 93％，这可作为未来研究的基准。

PerSHOP -- 波斯购物对话系统建模的数据集

PerSHOP -- A Persian dataset for shopping dialogue systems modeling

Existing multimodal task-oriented dialog data fails to demonstrate the
diverse expressions of user subjective preferences and recommendation acts in
the real-life shopping scenario. This paper introduces a new dataset SURE
(Multimodal Recommendation Dialog with SUbjective PREference), which contains
12K shopping dialogs in complex store scenes. The data is built in two phases
with human annotations to ensure quality and diversity. SURE is well-annotated
with subjective preferences and recommendation acts proposed by sales experts.
A comprehensive analysis is given to reveal the distinguishing features of
SURE. Three benchmark tasks are then proposed on the data to evaluate the
capability of multimodal recommendation agents. Based on the SURE, we propose a
baseline model, powered by a state-of-the-art multimodal model, for these
tasks.

该研究介绍了一个名为 SURE 的新数据集，其中包含人类主观偏好和销售专家提出的推荐行为，用于评估多模态推荐代理的能力，并提出了一个基于 SURE 的基准模型。