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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