Papers › TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance

TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance

17 May 2021ACL 2021 5arXiv:2105.07624archive 2025-07-28

Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, Tat-Seng Chua

Hybrid data combining both tabular and textual content (e.g., financial reports) are quite pervasive in the real world. However, Question Answering (QA) over such hybrid data is largely neglected in existing research. In this work, we extract samples from real financial reports to build a new large-scale QA dataset containing both Tabular And Textual data, named TAT-QA, where numerical reasoning is usually required to infer the answer, such as addition, subtraction, multiplication, division, counting, comparison/sorting, and the compositions. We further propose a novel QA model termed TAGOP, which is capable of reasoning over both tables and text. It adopts sequence tagging to extract relevant cells from the table along with relevant spans from the text to infer their semantics, and then applies symbolic reasoning over them with a set of aggregation operators to arrive at the final answer. TAGOPachieves 58.0% inF1, which is an 11.1% absolute increase over the previous best baseline model, according to our experiments on TAT-QA. But this result still lags far behind performance of expert human, i.e.90.8% in F1. It is demonstrated that our TAT-QA is very challenging and can serve as a benchmark for training and testing powerful QA models that address hybrid form data.

PaperPDFConference PDFCodeCode Syntology ran

In Syntology View this paper on Syntology: its repositories, every harvested function with whether it ran, its licence and the call to fetch it.

Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2105.07624")

Code

Syntology Ran 2 of 12 code samples harvested from 1 repository linked to this paper; 10 have no recorded run. Of those that ran: 2 ran · honoured contract.

By repository: official repository: 12 samples from 1 repository, 2 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

NExTplusplus/TAT-QA officialmentioned on GitHubpytorchMIT report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

12 samples harvested; 2 ran; 2 honoured the contract we drafted; 10 have no recorded run. Read from Syntology's graph 2026-09-25; that is when this build read the record, not when the samples ran.

2ran · honoured contract
10unverified

Licence: 0 of the 12 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from NExTplusplus/TAT-QA. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

warmup_constant NExTplusplus/TAT-QA/tag_op/tagop/optimizer.py official repository ran · honoured contract fingerprinted MIT (permissive) · e7d542062316094a · report
warmup_linear NExTplusplus/TAT-QA/tag_op/tagop/optimizer.py official repository ran · honoured contract fingerprinted MIT (permissive) · c58d57224530d17e · report
create_logger NExTplusplus/TAT-QA/tag_op/tagop/util.py official repository unverified MIT (permissive) · ea74b65463265ba2 · report
extract_gold_answers NExTplusplus/TAT-QA/tatqa_metric.py official repository unverified MIT (permissive) · 2be876d386453689 · report
extract_one_num_from_str NExTplusplus/TAT-QA/tatqa_utils.py official repository unverified MIT (permissive) · b591a51b9d4dd3bb · report
get_continuous_tag_slots NExTplusplus/TAT-QA/tag_op/tagop/modeling_tagop.py official repository unverified MIT (permissive) · f39958953701ff98 · report
get_span_tokens_from_paragraph NExTplusplus/TAT-QA/tag_op/tagop/modeling_tagop.py official repository unverified MIT (permissive) · 43ec29e1df123bda · report
get_span_tokens_from_table NExTplusplus/TAT-QA/tag_op/tagop/modeling_tagop.py official repository unverified MIT (permissive) · 275419aee12c926d · report
is_number NExTplusplus/TAT-QA/tatqa_utils.py official repository unverified MIT (permissive) · 1a0411f2ec690e26 · report
metric_max_over_ground_truths NExTplusplus/TAT-QA/tatqa_metric.py official repository unverified MIT (permissive) · 03692148eacb9928 · report
scale_to_num NExTplusplus/TAT-QA/tatqa_utils.py official repository unverified MIT (permissive) · 71b0d5b420628a19 · report
warmup_cosine NExTplusplus/TAT-QA/tag_op/tagop/optimizer.py official repository unverified MIT (permissive) · 35f7cddf90dd05d4 · report

Tasks

Question Answering

Datasets

Introduced by this paper, per the archive.

TAT-QA

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Question Answering TAT-QA TagOp Exact Match (EM) 50.1 #1 of 1 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections