---
annotations_creators:
  - derived
language:
  - eng
license: other
license_name: statcan-dialogue-retrieval-license
license_link: >-
  https://huggingface.co/datasets/McGill-NLP/statcan-dialogue-dataset-retrieval/blob/main/LICENSE.md
multilinguality: monolingual
task_categories:
  - text-retrieval
task_ids:
  - document-retrieval
tags:
  - table-retrieval
  - text
pretty_name: StatCan
config_names:
  - default
  - queries
  - corpus

dataset_info:
  - config_name: default
    features:
      - name: qid
        dtype: string
      - name: did
        dtype: string
      - name: score
        dtype: int32
    splits:
      - name: test
        num_bytes: 43500
        num_examples: 870
  - config_name: queries
    features:
      - name: _id
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: test_queries
        num_bytes: 829463
        num_examples: 870
  - config_name: corpus
    features:
      - name: _id
        dtype: string
      - name: title
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: corpus
        num_bytes: 40220365
        num_examples: 5907

configs:
  - config_name: default
    data_files:
      - split: test
        path: test_qrels.jsonl
  - config_name: queries
    data_files:
      - split: test_queries
        path: test_queries.jsonl
  - config_name: corpus
    data_files:
      - split: corpus
        path: corpus.jsonl
---

# StatCan Retrieval

This dataset is part of a Table + Text retrieval benchmark. Includes queries and relevance judgments across test split(s), with corpus in 1 format(s): `corpus`.

## Configs

| Config | Description | Split(s) |
|---|---|---|
| `default` | Relevance judgments (qrels): `qid`, `did`, `score` | `test` |
| `queries` | Query IDs and text | `test_queries` |
| `corpus` | Plain text corpus: `_id`, `title`, `text` | `corpus` |

## TableIR Benchmark Statistics

| Dataset | Structured | #Train | #Dev | #Test | #Corpus |
|---|:---:|---:|---:|---:|---:|
| OpenWikiTables | ✓ | 53.8k | 6.6k | 6.6k | 24.7k |
| NQTables | ✓ | 9.6k | 1.1k | 1k | 170k |
| FeTaQA | ✓ | 7.3k | 1k | 2k | 10.3k |
| OTT-QA (small) | ✓ | 41.5k | 2.2k | -- | 8.8k |
| MultiHierTT | ✗ | -- | 929 | -- | 9.9k |
| AIT-QA | ✗ | -- | -- | 515 | 1.9k |
| StatcanRetrieval | ✗ | -- | -- | 870 | 5.9k |
| watsonxDocsQA | ✗ | -- | -- | 30 | 1.1k |

## Citation

If you use **TableIR Eval: Table-Text IR Evaluation Collection**, please cite:

```bibtex
@misc{doshi2026tableir,
  title        = {TableIR Eval: Table-Text IR Evaluation Collection},
  author       = {Doshi, Meet and Boni, Odellia and Kumar, Vishwajeet and Sen, Jaydeep and Joshi, Sachindra},
  year         = {2026},
  institution  = {IBM Research},
  howpublished = {https://huggingface.co/collections/ibm-research/table-text-ir-evaluation},
  note         = {Hugging Face dataset collection}
}
```

All credit goes to original authors. Please cite their work:
```bibtex
@inproceedings{lu-etal-2023-statcan,
    title = "The {S}tat{C}an Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents",
    author = "Lu, Xing Han  and
      Reddy, Siva  and
      de Vries, Harm",
    editor = "Vlachos, Andreas  and
      Augenstein, Isabelle",
    booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.eacl-main.206/",
    doi = "10.18653/v1/2023.eacl-main.206",
    pages = "2799--2829",
    abstract = "We introduce the StatCan Dialogue Dataset consisting of 19,379 conversation turns between agents working at Statistics Canada and online users looking for published data tables. The conversations stem from genuine intents, are held in English or French, and lead to agents retrieving one of over 5000 complex data tables. Based on this dataset, we propose two tasks: (1) automatic retrieval of relevant tables based on a on-going conversation, and (2) automatic generation of appropriate agent responses at each turn. We investigate the difficulty of each task by establishing strong baselines. Our experiments on a temporal data split reveal that all models struggle to generalize to future conversations, as we observe a significant drop in performance across both tasks when we move from the validation to the test set. In addition, we find that response generation models struggle to decide when to return a table. Considering that the tasks pose significant challenges to existing models, we encourage the community to develop models for our task, which can be directly used to help knowledge workers find relevant tables for live chat users."
}
```