Research

What I’m most interested in is building systems that can infer knowledge and reason in the way that humans do, by creating frameworks that incorporate common sense knowledge and human-level inference. This includes modeling interactions between agents that first reason and respond with respect to goal-oriented information at hand, but then also allow world knowledge to alter their reasoning. More recently, I’m interested in learning semantic correspondences between language, images and mental depictions of concepts we encounter and learning semantic representations for structures in text.

Where I've Been

(Upcoming) DeepMind, London: Research Intern (Multi-agent Reinforcement Learning)
Advised by Yoram Bachrach. Summer 2020.

Google AI, Mountain View: Research Intern (Grounded Language and Learning)
Advised by Alex Ku and Jason Baldridge. Summer 2019.

Johns Hopkins University: Jelinek Summer Workshop on Speech and Language Technology (JSALT)
Advised by Ellie Pavlick, Brown University; Sam Bowman, New York University; Tal Linzen, Johns Hopkins University. Summer 2018.

University of Pennsylvania: Undergraduate Researcher
Advised by Ani Nenkova, University of Pennsylvania and Byron Wallace, Northeastern University. Summer 2017-18.

Max Planck Institute: Cornell, Maryland, Max Planck Pre-doctoral Research School (CMMRS)
Summer 2018.

Princeton University: Program in Algorithmic and Combinatorial Thinking (PACT)
Advised by Rajiv Gandhi, Rutgers University, Camden. Summer 2016.

Lectures and Invited Talks

Columbia University, Data Science Institue
Title: Learning from Patterns for Information Extraction for Medical Literature

Princeton University, PACT Summer Program
Title: Network Flows

Papers

2020

Grounding Language to Non-Markovian Tasks with No Supervision of Task Specifications.
Roma Patel, Ellie Pavlick, Stefanie Tellex.
RSS 2020.

Robot Object Retrieval with Contextual Natural Language Queries.
Thao Nguyen, Nakul Gopalan, Roma Patel, Matthew Corsaro, Ellie Pavlick, Stefanie Tellex.
RSS 2020.

2019

How to Get Past Sesame Street: Sentence-Level Pretraining Beyond Language Modeling
Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas Mccoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick and Samuel R. Bowman.
ACL 2019. PDF

Planning with State Abstractions for Non-Markovian Task Specifications
Yoonseon Oh, Roma Patel, Thao Nguyen, Baichuan Huang, Ellie Pavlick, Stefanie Tellex.
RSS 2019. PDF

Learning Visually Grounded Meaning Representations with Sketches
Roma Patel, Stephen Bach and Ellie Pavlick.
How2 Workshop at ICML 2019. PDF

Learning to Ground Language to Temporal Logical Form.
Roma Patel, Ellie Pavlick and Stefanie Tellex.
SpLU & RoboNLP Workshop at NAACL 2019. PDF

Probing What Different NLP Tasks Teach Machines about Function Word Comprehension
Najoung Kim, Roma Patel, Adam Poliak, Alex Wang, Patrick Xia, R. Thomas McCoy, Ian Tenney, Alexis Ross, Tal Linzen, Benjamin Van Durme, Samuel R. Bowman, Ellie Pavlick.
StarSEM. 2019. (Best Paper Award!) PDF

Looking for ELMo's Friends: Sentence-Level Pretraining Beyond Language Modeling.
Samuel R. Bowman, Ellie Pavlick, Edouard Grave, Benjamin Van Durme, Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, and Berlin Chen.
Unpublished manuscript. 2019. PDFi

2018

Modeling Ambiguity in Text: A Corpus of Legal Literature.
Roma Patel and Ani Nenkova.
Unpublished manuscript. 2018. PDF

A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature.
Benjamin Nye, Jessy Li, Roma Patel, Yinfei Yang, Iain Marshall, Ani Nenkova and Byron Wallace.
ACL 2018. PDF

Syntactic Patterns Improve Information Extraction for Medical Literature
Roma Patel, Yinfei Yang, Iain Marshall, Ani Nenkova and Byron Wallace.
NAACL 2018. PDF