Datasets:

zzh12138
/

CRT-QA

Tasks:

Table Question Answering

Languages:

English

Size Categories:

n<1K

License:

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Split (1)

train

The dataset viewer is not available for this split.

Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.

Error code:   FeaturesError
Exception:    ValueError
Message:      Not able to read records in the JSON file at hf://datasets/zzh12138/CRT-QA@ecc8b80766e46150634efa513a82ce4c3146e276/dataset.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['2-10311801-2.html.csv', '2-1064198-3.html.csv', '2-18843924-5.html.csv', '2-10660606-2.html.csv', '2-1851722-36.html.csv', '2-1122082-1.html.csv', '2-10758793-4.html.csv', '2-10265361-1.html.csv', '2-11734041-9.html.csv', '2-18564451-1.html.csv', '2-11215792-8.html.csv', '2-12866432-1.html.csv', '1-30153446-1.html.csv', '1-27553627-2.html.csv', '2-16388439-4.html.csv', '2-12307135-7.html.csv', '2-12660586-1.html.csv', '2-10879231-1.html.csv', '2-15412381-5.html.csv', '2-18517323-1.html.csv', '2-1129111-1.html.csv', '2-10516844-3.html.csv', '2-10289926-15.html.csv', '2-15619795-7.html.csv', '2-11667521-16.html.csv', '2-1222653-10.html.csv', '2-12218597-1.html.csv', '2-12057147-2.html.csv', '2-17159413-1.html.csv', '2-13076596-3.html.csv', '2-12514326-1.html.csv', '1-2857352-3.html.csv', '2-1673849-1.html.csv', '2-1699550-1.html.csv', '2-14611720-1.html.csv', '2-11622862-3.html.csv', '2-14305653-43.html.csv', '2-1585090-1.html.csv', '2-18615220-1.html.csv', '1-2468961-7.html.csv', '2-11456020-2.html.csv', '2-1122942-2.html.csv', '2-1818978-1.html.csv', '1-30062172-3.html.csv', '2-12152327-4.html.csv', '2-12757263-2.html.csv', '2-10724559-1.html.csv', '2-15502952-15.html.csv', '2-17290159-1.html.csv', '2-18756696-1.html.csv', '2-17759945-9.html.csv', '2-15531181-17.html.csv', '2-10910853-5.html.csv', '2-16387700-1.html.csv', '2-1354148-3.html.csv', '2-1820430-1.html.csv', '1-28962227-1.html.csv', '2-16878651-1.html.csv', '2-18921615-1.html.csv', '2-13536392-2.html.csv', '2-11296015-5.html.csv', '2-16514839-1.html.csv', '2-113549-2.html.csv', '2-11025881-1.html.csv', '1-2897457-3.html.csv', '2-14611590-3.html.csv', '2-1554464-3.html.csv', '2-15205941-2.html.csv', '1-27547668-2.html.csv', '2-1226502-2.html.csv', '1-27771406-1.html.csv', '2-12197750-6.html.csv', '2-18935018-1.html.csv', '2-15780049-10.html.csv', '2-12807827-2.html.csv', '1-2655016-4.html.csv', '2-17445673-2.html.csv', '2-12572989-1.html.csv', '2-17964087-2.html.csv', '2-14883-2.html.csv', '2-17389615-6.html.csv', '2-1064216-1.html.csv', '2-14820149-3.html.csv', '1-286271-1.html.csv', '2-12392766-3.html.csv', '2-1710991-1.html.csv', '2-17471066-1.html.csv', '2-17746881-1.html.csv', '2-15887683-4.html.csv', '2-16218498-1.html.csv', '1-23157997-13.html.csv', '2-11916083-27.html.csv', '2-1213811-1.html.csv', '2-187504-13.html.csv', '2-15089329-1.html.csv', '2-11677691-4.html.csv', '2-16400024-2.html.csv', '1-27484208-1.html.csv', '2-18890652-1.html.csv', '2-11344569-3.html.csv', '2-1222904-1.html.csv', '1-25887826-17.html.csv', '2-17455843-3.html.csv', '2-1689029-2.html.csv', '2-12962773-9.html.csv', '2-1226338-2.html.csv', '2-15427957-1.html.csv', '2-16593799-8.html.csv', '2-1097299-1.html.csv', '1-2933761-1.html.csv', '2-11566142-1.html.csv', '2-149693-1.html.csv', '2-14752049-4.html.csv', '2-15194193-3.html.csv', '1-25030512-41.html.csv', '1-2649597-1.html.csv', '2-18662026-3.html.csv', '2-12792876-1.html.csv', '2-1722194-5.html.csv', '2-1027162-1.html.csv', '2-10826072-21.html.csv', '2-1050189-1.html.csv', '2-18985137-1.html.csv', '2-15029747-1.html.csv', '2-153162-1.html.csv', '2-1805191-33.html.csv', '2-16142610-11.html.csv', '2-12614827-2.html.csv', '2-12450336-19.html.csv', '2-17832085-4.html.csv', '2-15524351-5.html.csv', '2-1825661-2.html.csv', '2-167235-4.html.csv', '2-1363705-1.html.csv', '2-16590486-3.html.csv', '2-1145226-5.html.csv', '2-17786294-1.html.csv', '2-157294-1.html.csv', '1-23297-3.html.csv', '2-13949437-2.html.csv', '2-17944591-1.html.csv', '2-10669284-1.html.csv', '2-17100961-62.html.csv', '2-1023035-3.html.csv', '2-14202514-1.html.csv', '2-11097420-1.html.csv', '2-1103715-1.html.csv', '2-1230478-2.html.csv', '1-25983027-1.html.csv', '1-24778847-2.html.csv', '2-154097-1.html.csv', '2-10577744-2.html.csv', '2-17381624-1.html.csv', '2-13054553-9.html.csv', '2-184334-2.html.csv', '2-12030247-2.html.csv', '2-17781704-3.html.csv', '2-14934885-5.html.csv', '2-15345530-1.html.csv', '2-17717981-1.html.csv', '2-14394530-1.html.csv', '2-13114949-5.html.csv', '2-10809157-8.html.csv', '2-12962773-5.html.csv', '2-16788123-5.html.csv', '2-1629175-1.html.csv', '2-13883437-3.html.csv', '2-11902366-5.html.csv', '2-13073611-2.html.csv', '2-11439940-2.html.csv', '2-18113463-7.html.csv', '2-11070660-5.html.csv', '2-11025881-3.html.csv', '2-16864968-7.html.csv', '1-28561455-1.html.csv', '2-11106562-3.html.csv', '2-16661199-2.html.csv', '2-12206491-10.html.csv', '1-27615445-1.html.csv', '2-11440693-2.html.csv', '2-17620547-5.html.csv', '2-1354940-2.html.csv', '2-17198719-1.html.csv', '2-15982651-1.html.csv', '1-25030512-36.html.csv', '2-189598-7.html.csv', '2-1795208-5.html.csv', '2-12082591-3.html.csv', '2-1818918-2.html.csv', '2-16915939-1.html.csv', '2-11442591-4.html.csv', '2-1228353-1.html.csv', '2-1235868-1.html.csv', '1-29273390-1.html.csv', '2-11803648-7.html.csv', '2-11527967-4.html.csv', '1-25246990-5.html.csv', '1-24648983-1.html.csv', '2-15367861-2.html.csv', '2-14292964-20.html.csv', '2-17626199-10.html.csv', '1-27277284-8.html.csv', '2-13052263-3.html.csv', '2-11240028-1.html.csv', '1-2668199-2.html.csv', '2-11149631-1.html.csv', '2-15707829-3.html.csv', '2-18662673-8.html.csv', '1-23206812-1.html.csv', '2-18400-2.html.csv', '2-1281645-1.html.csv', '2-142178-1.html.csv', '2-1226331-1.html.csv', '2-18682634-1.html.csv', '2-1491582-1.html.csv', '2-14728538-1.html.csv', '2-10044708-2.html.csv', '2-17416221-1.html.csv', '2-11860857-3.html.csv', '2-15805928-1.html.csv', '2-15678216-2.html.csv', '2-10784488-2.html.csv', '2-16956150-1.html.csv', '2-16069874-6.html.csv', '2-17304621-11.html.csv', '1-2538117-5.html.csv', '2-17993994-4.html.csv', '2-12606666-1.html.csv', '2-12214488-5.html.csv', '1-23408094-14.html.csv', '2-14420686-3.html.csv', '2-10976484-1.html.csv', '2-18518150-1.html.csv', '2-140725-1.html.csv', '2-12195931-1.html.csv', '2-1145513-24.html.csv', '1-27910411-1.html.csv', '2-1421760-1.html.csv', '2-10728418-4.html.csv', '2-15016411-1.html.csv', '2-106104-1.html.csv', '2-1074011-2.html.csv', '2-14640372-4.html.csv', '2-13894411-7.html.csv', '1-27547668-3.html.csv', '1-245801-1.html.csv', '2-1431450-2.html.csv', '2-13619558-1.html.csv', '2-12982226-3.html.csv', '2-1034685-1.html.csv', '2-12715053-1.html.csv', '1-2570269-3.html.csv', '1-2417308-4.html.csv', '2-18792948-10.html.csv', '2-17113304-1.html.csv', '1-28787871-3.html.csv', '2-15078664-2.html.csv', '2-10791018-1.html.csv', '2-1238577-1.html.csv', '1-26166836-1.html.csv', '1-262383-1.html.csv', '2-169568-1.html.csv', '2-10637415-1.html.csv', '2-12392607-3.html.csv', '1-2985987-2.html.csv', '1-27455867-1.html.csv', '1-23248420-1.html.csv', '2-11963536-8.html.csv', '2-16432704-2.html.csv', '2-11308227-3.html.csv', '2-14892957-1.html.csv', '2-14160327-3.html.csv', '2-18259953-7.html.csv', '2-12146269-6.html.csv', '2-10670367-2.html.csv', '2-1392092-5.html.csv', '2-1156744-1.html.csv', '2-1096793-8.html.csv', '2-17572011-1.html.csv', '1-229917-2.html.csv', '2-11552751-3.html.csv', '2-1430940-3.html.csv', '2-10577579-2.html.csv', '2-1152185-1.html.csv', '2-1219760-3.html.csv', '2-11154187-2.html.csv', '2-11051845-1.html.csv', '2-18562984-4.html.csv', '2-18629727-2.html.csv', '2-14201926-1.html.csv', '2-17408152-2.html.csv', '2-14271063-1.html.csv', '2-15715109-45.html.csv', '2-18381900-1.html.csv', '2-1861430-3.html.csv', '2-17537584-1.html.csv', '2-1123371-2.html.csv', '2-12032042-1.html.csv', '2-1107059-1.html.csv', '1-30108346-1.html.csv', '2-18781567-2.html.csv', '2-14749151-1.html.csv', '2-12988799-9.html.csv', '2-15220147-3.html.csv', '2-18639024-14.html.csv', '2-14890430-2.html.csv', '2-14986292-1.html.csv', '2-1722347-2.html.csv', '2-17599325-1.html.csv', '2-18259953-6.html.csv', '2-1620397-5.html.csv', '2-11138928-1.html.csv', '2-1053453-8.html.csv', '2-15494883-26.html.csv', '2-12275654-1.html.csv', '2-10621888-3.html.csv', '2-1803594-1.html.csv', '1-2417308-3.html.csv', '2-18084-3.html.csv', '2-10932739-2.html.csv', '2-16382861-1.html.csv', '2-172426-1.html.csv', '2-1725690-2.html.csv', '2-17747000-1.html.csv', '2-11847348-3.html.csv', '2-17445415-2.html.csv', '2-16653153-30.html.csv', '2-1096038-12.html.csv', '2-13566976-7.html.csv', '2-15201858-1.html.csv', '2-12058560-1.html.csv', '2-18726561-5.html.csv', '1-2602958-4.html.csv', '2-12076353-1.html.csv', '2-18154969-1.html.csv', '2-15826161-2.html.csv', '2-16910989-5.html.csv', '2-11713303-2.html.csv', '2-1198175-1.html.csv', '2-11921877-4.html.csv', '2-1598242-3.html.csv', '2-16953587-4.html.csv', '2-10753786-4.html.csv', '2-16474033-6.html.csv', '2-17634218-19.html.csv', '2-15184672-3.html.csv', '2-15544826-1.html.csv', '2-13117332-1.html.csv', '2-14562754-1.html.csv', '2-1755878-2.html.csv', '2-1219581-1.html.csv', '2-1664787-1.html.csv', '2-16578883-3.html.csv', '2-11803648-22.html.csv', '1-245800-2.html.csv', '2-1235742-1.html.csv', '2-14303579-16.html.csv', '2-10746808-4.html.csv', '2-18078622-1.html.csv']. Select the correct one and provide it as `field='XXX'` to the dataset loading method. 
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 241, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2216, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1239, in _head
                  return _examples_to_batch(list(self.take(n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1389, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1044, in __iter__
                  yield from islice(self.ex_iterable, self.n)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 282, in __iter__
                  for key, pa_table in self.generate_tables_fn(**self.kwargs):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 170, in _generate_tables
                  raise ValueError(
              ValueError: Not able to read records in the JSON file at hf://datasets/zzh12138/CRT-QA@ecc8b80766e46150634efa513a82ce4c3146e276/dataset.json. You should probably indicate the field of the JSON file containing your records. This JSON file contain the following fields: ['2-10311801-2.html.csv', '2-1064198-3.html.csv', '2-18843924-5.html.csv', '2-10660606-2.html.csv', '2-1851722-36.html.csv', '2-1122082-1.html.csv', '2-10758793-4.html.csv', '2-10265361-1.html.csv', '2-11734041-9.html.csv', '2-18564451-1.html.csv', '2-11215792-8.html.csv', '2-12866432-1.html.csv', '1-30153446-1.html.csv', '1-27553627-2.html.csv', '2-16388439-4.html.csv', '2-12307135-7.html.csv', '2-12660586-1.html.csv', '2-10879231-1.html.csv', '2-15412381-5.html.csv', '2-18517323-1.html.csv', '2-1129111-1.html.csv', '2-10516844-3.html.csv', '2-10289926-15.html.csv', '2-15619795-7.html.csv', '2-11667521-16.html.csv', '2-1222653-10.html.csv', '2-12218597-1.html.csv', '2-12057147-2.html.csv', '2-17159413-1.html.csv', '2-13076596-3.html.csv', '2-12514326-1.html.csv', '1-2857352-3.html.csv', '2-1673849-1.html.csv', '2-1699550-1.html.csv', '2-14611720-1.html.csv', '2-11622862-3.html.csv', '2-14305653-43.html.csv', '2-1585090-1.html.csv', '2-18615220-1.html.csv', '1-2468961-7.html.csv', '2-11456020-2.html.csv', '2-1122942-2.html.csv', '2-1818978-1.html.csv', '1-30062172-3.html.csv', '2-12152327-4.html.csv', '2-12757263-2.html.csv', '2-10724559-1.html.csv', '2-15502952-15.html.csv', '2-17290159-1.html.csv', '2-18756696-1.html.csv', '2-17759945-9.html.csv', '2-15531181-17.html.csv', '2-10910853-5.html.csv', '2-16387700-1.html.csv', '2-1354148-3.html.csv', '2-1820430-1.html.csv', '1-28962227-1.html.csv', '2-16878651-1.html.csv', '2-18921615-1.html.csv', '2-13536392-2.html.csv', '2-11296015-5.html.csv', '2-16514839-1.html.csv', '2-113549-2.html.csv', '2-11025881-1.html.csv', '1-2897457-3.html.csv', '2-14611590-3.html.csv', '2-1554464-3.html.csv', '2-15205941-2.html.csv', '1-27547668-2.html.csv', '2-1226502-2.html.csv', '1-27771406-1.html.csv', '2-12197750-6.html.csv', '2-18935018-1.html.csv', '2-15780049-10.html.csv', '2-12807827-2.html.csv', '1-2655016-4.html.csv', '2-17445673-2.html.csv', '2-12572989-1.html.csv', 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This repository contains the CRT-QA dataset, which includes question-answer pairs that require complex reasoning over tabular data. 🚀

About the Dataset and Paper

Title: CRT-QA: A Dataset of Complex Reasoning Question Answering over Tabular Data
Conference: EMNLP 2023
Authors: Zhehao Zhang, Xitao Li, Yan Gao, Jian-Guang Lou 👩‍💼👨‍💼
Affiliation: Dartmouth College, Xi'an Jiaotong University, Microsoft Research Asia 🏢

Data Format

The data is stored in a json file, structured with the following fields for each datapoint (keyed by a .csv file table):

Question name, Title, step1, step2, step3, step4, Answer, Directness, Composition Type

Question name: The text of the question
Title: The title of the table that the question refers to
step1 to step4: Steps describing the reasoning process and operations used to answer the question
- type: Operation or Reasoning
- name: Name of the specific operation or reasoning type
- detail: Additional details about the step
Answer: The answer text
Directness: Explicit or Implicit question
Composition Type: Bridging, Intersection, or Comparison

Reasoning and Operations

The reasoning and operations referenced in the step fields come from a defined taxonomy:

Operations:

Indexing
Filtering
Grouping
Sorting

Reasoning:

Grounding
Auto-categorization
Temporal Reasoning
Geographical/Spatial Reasoning
Aggregating
Arithmetic
Reasoning with Quantifiers
Other Commonsense Reasoning

Contact 📧

For inquiries or updates about this repository, please contact [zhehao.zhang.gr@dartmouth.edu]. 📬

Citation

If you use this dataset in your research, please cite the following paper:

@inproceedings{zhang-etal-2023-crt,
    title = "{CRT}-{QA}: A Dataset of Complex Reasoning Question Answering over Tabular Data",
    author = "Zhang, Zhehao  and
      Li, Xitao  and
      Gao, Yan  and
      Lou, Jian-Guang",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.132",
    doi = "10.18653/v1/2023.emnlp-main.132",
    pages = "2131--2153",
    abstract = "Large language models (LLMs) show powerful reasoning abilities on various text-based tasks. However, their reasoning capability on structured data such as tables has not been systematically explored. In this work, we first establish a comprehensive taxonomy of reasoning and operation types for tabular data analysis. Then, we construct a complex reasoning QA dataset over tabular data, named CRT-QA dataset (Complex Reasoning QA over Tabular data), with the following unique features: (1) it is the first Table QA dataset with multi-step operation and informal reasoning; (2) it contains fine-grained annotations on questions{'} directness, composition types of sub-questions, and human reasoning paths which can be used to conduct a thorough investigation on LLMs{'} reasoning ability; (3) it contains a collection of unanswerable and indeterminate questions that commonly arise in real-world situations. We further introduce an efficient and effective tool-augmented method, named ARC (Auto-exemplar-guided Reasoning with Code), to use external tools such as Pandas to solve table reasoning tasks without handcrafted demonstrations. The experiment results show that CRT-QA presents a strong challenge for baseline methods and ARC achieves the best result.",
}

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