PlotQA: Reasoning over Scientific Plots

TL;DR

This paper introduces PlotQA, a large dataset for reasoning over real-world scientific plots, and proposes a hybrid model that effectively handles out-of-vocabulary questions and complex reasoning tasks.

Contribution

The paper presents PlotQA, a new dataset with 28.9 million QA pairs over real-world plots, and a hybrid model that improves reasoning over such plots, especially for out-of-vocabulary questions.

Findings

Existing models perform poorly on PlotQA with single-digit accuracy.

The proposed hybrid model achieves 22.52% accuracy on PlotQA, surpassing previous models.

On the DVQA dataset, the model improves accuracy from 46% to 58%.

Abstract

Existing synthetic datasets (FigureQA, DVQA) for reasoning over plots do not contain variability in data labels, real-valued data, or complex reasoning questions. Consequently, proposed models for these datasets do not fully address the challenge of reasoning over plots. In particular, they assume that the answer comes either from a small fixed size vocabulary or from a bounding box within the image. However, in practice, this is an unrealistic assumption because many questions require reasoning and thus have real-valued answers which appear neither in a small fixed size vocabulary nor in the image. In this work, we aim to bridge this gap between existing datasets and real-world plots. Specifically, we propose PlotQA with 28.9 million question-answer pairs over 224,377 plots on data from real-world sources and questions based on crowd-sourced question templates. Further, 80.76% of the out-of-vocabulary (OOV) questions in PlotQA have answers that are not in a fixed vocabulary. Analysis of existing models on PlotQA reveals that they cannot deal with OOV questions: their overall accuracy on our dataset is in single digits. This is not surprising given that these models were not designed for such questions. As a step towards a more holistic model which can address fixed vocabulary as well as OOV questions, we propose a hybrid approach: Specific questions are answered by choosing the answer from a fixed vocabulary or by extracting it from a predicted bounding box in the plot, while other questions are answered with a table question-answering engine which is fed with a structured table generated by detecting visual elements from the image. On the existing DVQA dataset, our model has an accuracy of 58%, significantly improving on the highest reported accuracy of 46%. On PlotQA, our model has an accuracy of 22.52%, which is significantly better than state of the art models.