TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data
Fan Zhou, Mengkang Hu, Haoyu Dong, Zhoujun Cheng, Fan Cheng, Han Shi, Dongmei Zhang · 2022
Existing auto-regressive pre-trained language models (PLMs) like T5 and BART, have been well applied to table question answering by UNIFIEDSKG and TAPEX, respectively, and demonstrated state-of-the-art results on multiple benchmarks.However, auto-regressive PLMs are challenged by recent emerging numerical reasoning datasets, such as TAT-QA, due to the error-prone implicit calculation.In this paper, we present TACUBE, to precompute aggregation/arithmetic results for the table in advance, so that they are handy and readily available for PLMs to answer numerical reasoning questions.TACUBE systematically and comprehensively covers a collection of computational operations over table segments.By simply concatenating TACUBE to the input sequence of PLMs, it shows significant experimental effectiveness.TACUBE promotes the F1 score from 49.6% to 66.2% on TAT-QA and achieves new state-of-the-art results on WikiTQ (59.6% denotation accuracy).TACUBE 's improvements on numerical reasoning cases are even more notable: on TAT-QA, TACUBE promotes the exact match accuracy of BART-large by 39.6% on sum, 52.5% on average, 36.6% on subtraction and 22.2% on division.We believe that TACUBE is a general and portable pre-computation solution that can be potentially integrated into various numerical reasoning frameworks.Data and code will be available at https://github.com/ microsoft/TaCube.