ibm-research/StatCanDialogueRetrieval

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:

@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:

@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."
}