CLEVR Parser swMATH ID: 35072 Software Authors: Raeid Saqur, Ameet Deshpande Description: CLEVR Parser: A Graph Parser Library for Geometric Learning on Language Grounded Image Scenes. The CLEVR dataset has been used extensively in language grounded visual reasoning in Machine Learning (ML) and Natural Language Processing (NLP) domains. We present a graph parser library for CLEVR, that provides functionalities for object-centric attributes and relationships extraction, and construction of structural graph representations for dual modalities. Structural order-invariant representations enable geometric learning and can aid in downstream tasks like language grounding to vision, robotics, compositionality, interpretability, and computational grammar construction. We provide three extensible main components - parser, embedder, and visualizer that can be tailored to suit specific learning setups. We also provide out-of-the-box functionality for seamless integration with popular deep graph neural network (GNN) libraries. Additionally, we discuss downstream usage and applications of the library, and how it accelerates research for the NLP research community. Homepage: https://arxiv.org/abs/2009.09154 Source Code: https://github.com/raeidsaqur/clevr-parser Dependencies: Python Keywords: arXiv_cs.CL; Graph Parser Library; CLEVR dataset; Machine Learning; ML; Natural Language Processing; NLP; deep graph neural network; GNN Related Software: StanfordCoreNLP; XLNet; BERT; CLEVR dataset; PyTorch; NLTK; spaCy; Python Cited in: 0 Publications Standard Articles 1 Publication describing the Software Year CLEVR Parser: A Graph Parser Library for Geometric Learning on Language Grounded Image Scenes Raeid Saqur, Ameet Deshpande 2020