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Published in Findings of EMNLP , EMNLP-2020, Virtual Event, 2020
We introduce a method called attention-guidance, which uses intuitive priors to modify self-attention heads in Transformer models. Our approach gives faster convergence and better downstream performance, and allows convergence of large models on as little as 4 GPUs.
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Published in 2nd Workshop for Natural Language Processing Open Source Software (NLP-OSS) , EMNLP-2020, Virtual Event, 2020
This paper describes an open-source graph parser for the CLEVR dataset.
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Published in Young Researchers' Symposium, CoDS-COMAD 2019, Kolkata, India, 2019
We propose and experiment with methods to combine the use of ontologies and function approximaters. We pose the methods as a type of data-knowledge trade-off and achieve superior performance on muliple tasks
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Published in 2019 International Joint Conference on Neural Networks (IJCNN), 2018
This paper details the approach which achieved state-of-the-art performance on FigureQA Dataset
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