| --- |
| license: cc-by-nc-4.0 |
| language: |
| - fa |
| pretty_name: PerSHOP |
| size_categories: |
| - 10K<n<100K |
| task_categories: |
| - text-classification |
| - token-classification |
| - text-generation |
| task_ids: |
| - intent-classification |
| - named-entity-recognition |
| - dialogue-modeling |
| tags: |
| - task-oriented-dialogue |
| - shopping |
| - persian |
| - dialogue-state-tracking |
| - slot-filling |
| configs: |
| - config_name: dialogues |
| default: true |
| data_files: |
| - split: train |
| path: data/dialogues/train.jsonl |
| - split: validation |
| path: data/dialogues/validation.jsonl |
| - split: test |
| path: data/dialogues/test.jsonl |
| - config_name: products |
| data_files: |
| - split: database |
| path: data/products/products.jsonl |
| - config_name: slots |
| data_files: |
| - split: descriptions |
| path: data/products/slots.jsonl |
| --- |
| |
| # 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 |
|
|
| ```python |
| 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](https://creativecommons.org/licenses/by-nc/4.0/) |
| 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 |
|
|
| ```bibtex |
| @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 |
|
|
