I am a Research Fellow at Microsoft Research India, where I work with Dr. Navin Goyal. My primary area of research is machine learning and computational linguistics. I am interested in computational methods to improve our understanding of the structure of natural language and to build artificial systems that can learn to process natural language the way we humans do. I work on problems around grammar induction, compositionality, semantic parsing, and analysis of deep learning models used in NLP.

Before I joined MSR, I spent a wonderful semester working with Dr. Partha Talukdar at the Machine and Language Learning (MALL) Lab in IISc. Prior to that, I spent a summer at A*STAR in Singapore where I worked with Dr. Anders Skanderup. In the summer of 2016, I worked with Dr. Chris Mungall and Dr. Dan Keith as a Google Summer of Code student. I (used to) occasionally solve problems on websites like Kaggle and answer questions on stats.stackexchange forum. I graduated with B.E. (Hons.) in Computer Science and Int. M.Sc. (Hons.) in Biological Science from BITS Pilani, India in 2019. For more details, refer to my CV or drop me an email.

Coming fall, I will be joining the University of Oxford as a PhD student in Computer Science.
  Google Scholar|   Semantic Scholar

Are NLP Models really able to Solve Simple Math Word Problems?
Arkil Patel, Satwik Bhattamishra, Navin Goyal
pdf code abstract

On the Practical Ability of RNNs to Recognize Hierarchical Languages
Satwik Bhattamishra, Kabir Ahuja, Navin Goyal
COLING'20 [Recipient of the Best Short Paper Award]
pdf code abstract

Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities
Pratik Joshi, Christain Barnes, Sebastin Santy, Simran Khanuja, Sanket Shah, Anirudh Srinivasan, Satwik Bhattamishra, Sunayana Sitaram, Monojit Choudhury, Kalika Bali
pdf abstract cite

Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation
Ashutosh Kumar*, Satwik Bhattamishra*, Manik Bhandari, Partha Talukdar
pdf code abstract


An easy to use python library with implementations of a set of tested optimization and regularization methods of NMF. Implemented Algorithms include graph regularized NMF, probabilistic NMF, a first-order primal-dual algorithm ...etc

A python package available in pip with modules for sampling from Determinantal Point Processes (DPP). Contains implementations of algorithms to sample from DPPs that encourage diversity in the selection of a subset of points from a grounded superset.

Reviewer   EMNLP 2020   NAACL 2021
Sub-Reviewer   CoNLL 2019
BITS Pilani
2014 - 2019
Google Summer of Code
A*STAR, Singapore
Indian Institute of Science
Microsoft Research India
2019 - Present
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