PerSHOP: A Multi-Domain Persian Dataset for Shopping Dialogue Systems

PerSHOP is a Persian task-oriented dialogue dataset in the shopping domain. Each dialogue is a conversation between a customer and a seller who fill a shopping basket from a fixed product database. Utterances are annotated with domain, intent/dialogue act, product category and inline slot spans.

This is the official release maintained by the authors, with fixed dialogue-level train/validation/test splits and the product database used to collect the dialogues.

Dialogues	1,061
Utterances	21,923
Domains	14 product domains + chit-chat
Intents / dialogue acts	20
Slot types	27
Products in the database	750
Mean utterances per dialogue	20.7

Collection

The dialogues were collected at the University of Tehran through a web platform built for this purpose. About 120 volunteer students (out of roughly 340 enrolled in the Data Structures and Natural Language Processing courses over three semesters) took the customer and seller roles and conducted shopping conversations over a shared product database, following a written guideline. The dialogues were then annotated by the first author.

The conversations are role-played; the released data contains only the speaker role (user or system) and no identifiers of the participants.

Splits

Split	Dialogues	Utterances
train	849	17,482
validation	106	2,242
test	106	2,199

• The split is made at the dialogue level: all utterances of a dialogue are in the same split, so no dialogue context leaks into evaluation data.
• 80/10/10, stratified by each dialogue's primary domain (its most frequent product domain), with a fixed seed (42).
• The dialogue IDs of each split are listed in splits/, and scripts/make_splits.py rebuilds the release exactly from the original spreadsheets.

Please report results on the test split and use validation for model selection.

Data fields

dialogues (one row per utterance)

Field	Description
utterance_id	Utterance ID from the original release (unique; IDs are not contiguous)
dialogue_id	Dialogue ID (1–1061)
turn_index	Position of the utterance within its dialogue, from 0
speaker	user (customer) or system (seller)
domain	Domain in Persian, as annotated (Chats for chit-chat turns)
domain_en	English name of the domain
intent	Intent or dialogue act, e.g. Purchase, Inform, Ask_slot, Ask_product, Add_product
super_product	Product category the turn is about, e.g. milk, water (null for chit-chat)
text	Utterance text with annotation markup removed
annotated_text	Annotated utterance; slots are marked inline as [span](slot) (see Corrections)
raw_text	RawText column of the original release, kept unchanged
entities	Slot spans parsed from annotated_text: value, slot, and start/end character offsets in text

products

The product database (750 items). Each record has id, full_name, domain, domain_en, super_product, product, price and the attribute slots that apply to that product (e.g. brand, flavor, volume, weight, fat).

slots

Names and English descriptions of the product attribute slots.

Usage

from datasets import load_dataset

dialogues = load_dataset("KeyvanMahmoudi/PerSHOP", "dialogues")
products = load_dataset("KeyvanMahmoudi/PerSHOP", "products", split="database")

Corrections

Compared with the original spreadsheet, this release applies a small set of documented corrections, listed in scripts/corrections.json:

• 13 utterances had slot markup that could not be parsed (e.g. (volume)[1.5], a missing bracket, or a missing slot label). Their syntax was fixed so the annotated spans are recoverable; in two of them (utterance_id 7835 and
  7879. the product and bag_number labels had been attached to the wrong spans and were swapped back.
• 2 empty user utterances (utterance_id 12536 and 13069) were removed.
• The slot super-product was renamed super_product, matching the naming of all other slots.

raw_text is unchanged. In 242 cases an utterance follows another utterance by the same speaker within a dialogue; these are kept as collected.

License

The dataset (dialogues, annotations and product database) is released under the Creative Commons Attribution-NonCommercial 4.0 International license. Research and other non-commercial use is permitted with citation of the paper below.

Commercial use — including training, fine-tuning or deploying models for commercial products or services — is not covered by this license and requires a separate written agreement with the authors.

Copies of PerSHOP published by third parties are not maintained by the authors; this license applies to all copies.

Citation

@inproceedings{mahmoudi2026pershop,
  title     = {PerSHOP: A Multi-Domain Persian Dataset for Shopping Dialogue Systems},
  author    = {Mahmoudi, Keyvan and Faili, Heshaam},
  booktitle = {2026 International Interdisciplinary Conference on Artificial Intelligence: Engineering, Health, Finance and Humanities (IICAI)},
  pages     = {1--6},
  year      = {2026},
  address   = {Tehran, Iran},
  doi       = {10.1109/IICAI70155.2026.11620935}
}

@article{mahmoudi2024pershop,
  title   = {PerSHOP -- A Persian dataset for shopping dialogue systems modeling},
  author  = {Mahmoudi, Keyvan and Faili, Heshaam},
  journal = {arXiv preprint arXiv:2401.00811},
  year    = {2024}
}

Links

• Paper (IEEE): https://doi.org/10.1109/IICAI70155.2026.11620935
• Preprint: https://arxiv.org/abs/2401.00811
• Code and original files: https://github.com/keyvanmahmoudi/PerSHOP