Chart question answering (CQA) is a newly proposed visual question answering(VQA) task where an algorithm must answer questions about data visualizations,e.g. bar charts, pie charts, and line graphs. CQA requires capabilities thatnatural-image VQA algorithms lack: fine-grained measurements, optical characterrecognition, and handling out-of-vocabulary words in both questions andanswers. Without modifications, state-of-the-art VQA algorithms perform poorlyon this task. Here, we propose a novel CQA algorithm called parallel recurrentfusion of image and language (PReFIL). PReFIL first learns bimodal embeddingsby fusing question and image features and then intelligently aggregates theselearned embeddings to answer the given question. Despite its simplicity, PReFILgreatly surpasses state-of-the art systems and human baselines on both theFigureQA and DVQA datasets. Additionally, we demonstrate that PReFIL can beused to reconstruct tables by asking a series of questions about a chart.