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

Wenqiang Lei

Abstract:

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 their
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. TAGOP achieves 58.0% in F1, 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 the performance of human
expert, i.e. 90.8% in F1. It demonstrates
that our TAT-QA is very challenging and can
serve as a benchmark for training and testing powerful QA models that address hybrid
data. Our dataset is publicly available for noncommercial use at https://nextplusplus.
github.io/TAT-QA/.