How Robust are the Tabular QA Models for Scientific Tables? A Study using Customized Dataset
This addresses the lack of robustness benchmarks for QA models on scientific tables, which is an incremental contribution for researchers in natural language processing and scientific information retrieval.
The authors tackled the problem of evaluating question-answering models on scientific tabular data by creating a new dataset called SciTabQA with 822 question-answer pairs, and found that existing state-of-the-art models perform poorly with a best F1 score of only 0.462.
Question-answering (QA) on hybrid scientific tabular and textual data deals with scientific information, and relies on complex numerical reasoning. In recent years, while tabular QA has seen rapid progress, understanding their robustness on scientific information is lacking due to absence of any benchmark dataset. To investigate the robustness of the existing state-of-the-art QA models on scientific hybrid tabular data, we propose a new dataset, "SciTabQA", consisting of 822 question-answer pairs from scientific tables and their descriptions. With the help of this dataset, we assess the state-of-the-art Tabular QA models based on their ability (i) to use heterogeneous information requiring both structured data (table) and unstructured data (text) and (ii) to perform complex scientific reasoning tasks. In essence, we check the capability of the models to interpret scientific tables and text. Our experiments show that "SciTabQA" is an innovative dataset to study question-answering over scientific heterogeneous data. We benchmark three state-of-the-art Tabular QA models, and find that the best F1 score is only 0.462.