OpenWikiTablesRetrieval

# OpenWikiTables Retrieval This dataset is part of a Table + Text retrieval benchmark. Includes queries and relevance judgments across test split(s), with corpus in 3 format(s): `corpus_linearized`, `corpus_md`, `corpus_structure`. ## Configs | Config | Description | Split(s) | |---|---|---| | `default` | Relevance judgments (qrels): `qid`, `did`, `score` | `test` | | `queries` | Query IDs and text | `test_queries` | | `corpus_linearized` | Linearized table representation | `corpus_linearized` | | `corpus_md` | Markdown table representation | `corpus_md` | | `corpus_structure` | Structured corpus with `headers`, `cells`, `meta_data`. `text` field corresponds to linearized Text + Table. | `corpus_structure` | ## `corpus_structure` additional fields | Field | Type | Description | |---|---|---| | `meta_data` | string | Table metadata / caption | | `headers` | list[string] | Column headers | | `cells` | list[string] | Flattened cell values | ## 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{kweon-etal-2023-open, title = "Open-{W}iki{T}able : Dataset for Open Domain Question Answering with Complex Reasoning over Table", author = "Kweon, Sunjun and Kwon, Yeonsu and Cho, Seonhee and Jo, Yohan and Choi, Edward", editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Findings of the Association for Computational Linguistics: ACL 2023", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.findings-acl.526/", doi = "10.18653/v1/2023.findings-acl.526", pages = "8285--8297", abstract = "Despite recent interest in open domain question answering (ODQA) over tables, many studies still rely on datasets that are not truly optimal for the task with respect to utilizing structural nature of table. These datasets assume answers reside as a single cell value and do not necessitate exploring over multiple cells such as aggregation, comparison, and sorting. Thus, we release Open-WikiTable, the first ODQA dataset that requires complex reasoning over tables. Open-WikiTable is built upon WikiSQL and WikiTableQuestions to be applicable in the open-domain setting. As each question is coupled with both textual answers and SQL queries, Open-WikiTable opens up a wide range of possibilities for future research, as both reader and parser methods can be applied. The dataset is publicly available." } ```