MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data
Yilun Zhao, Yunxiang Li, Chenying Li, Rui Zhang

TL;DR
MultiHiertt is a new large-scale benchmark for complex numerical reasoning over multi-hierarchical tabular and textual data, particularly in financial reports, designed to challenge existing models and promote progress in hybrid data question answering.
Contribution
The paper introduces MultiHiertt, a novel benchmark with multi-hierarchical tables and textual data, and proposes MT2Net, a new model for complex numerical reasoning tasks.
Findings
Existing models perform poorly on MultiHiertt, lagging behind human performance.
MultiHiertt's complexity reveals limitations of current QA models in multi-step reasoning.
The dataset provides detailed annotations to facilitate research in numerical reasoning.
Abstract
Numerical reasoning over hybrid data containing both textual and tabular content (e.g., financial reports) has recently attracted much attention in the NLP community. However, existing question answering (QA) benchmarks over hybrid data only include a single flat table in each document and thus lack examples of multi-step numerical reasoning across multiple hierarchical tables. To facilitate data analytical progress, we construct a new large-scale benchmark, MultiHiertt, with QA pairs over Multi Hierarchical Tabular and Textual data. MultiHiertt is built from a wealth of financial reports and has the following unique characteristics: 1) each document contain multiple tables and longer unstructured texts; 2) most of tables contained are hierarchical; 3) the reasoning process required for each question is more complex and challenging than existing benchmarks; and 4) fine-grained…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Text Analysis Techniques · Stock Market Forecasting Methods · Topic Modeling
