What’s New?

Nov 2019 - One new preprint submitted to arXiv
Nov 2019 - One paper accepted to AAAI
Sep 2019 - Gave a talk at WeCNLP 2019
Aug 2019 - Three papers accepted to EMNLP 2019 1 2 3
Jun 2019 - Two new preprints submitted to arXiv 1 2
Jun 2019 - Organized NeuralGen 2019 @ NAACL 2019
Apr 2019 - One paper accepted to ACL 2019
Feb 2019 - One paper accepted to NAACL 2019
Aug 2018 - One paper accepted to EMNLP 2018
May 2018 - Talk at NW-NLP 2018
Apr 2018 - Two papers accepted to ACL 2018 1 2
Mar 2018 - Joined AI2 to work on common sense!
Feb 2018 - Two papers accepted to NAACL 2018 1 2
Jan 2018 - One paper accepted to ICLR 2018
Dec 2017 - Won an AI2 Key Scientific Challenges Award

Projects

Commonsense Acquisition from Language

Machines must be able to acquire relevant and correct commonsense for an unbounded set of situations. We explore how to capture this knowledge at scale.

Procedural Language Understanding with Commonsense Simulation

Understanding procedural text such as instructions or stories requires anticipating the implicit causal effects described by the events in the text, necessitating new classes of algorithms that can learn to simulate these effects directly.

Language Generation with Pragmatic Discriminator Networks

Content and style coherence of neural language generators deteriorates dramatically when generating long text. We design discriminator models that change language generation objectives to encourage long-term coherence.

Document Summarization with Discourse Understanding

Abstractive summarization requires understanding the latent discourse properties of how information is presented. We develop new approaches for integrating these inductive biases into summarization architectures.

Scientific Process Understanding

Scientific texts describe complex interactions between entities and use specific non-generalizable terminology. We explore methods for learning to understand this type of language.

Learning Prototypical Event Structure from Photo Albums

Activities and events in our lives are structural, be it a vacation, a camping trip, or a wedding. While individual details vary, there are characteristic patterns that are specific to each of these scenarios

Publications

(2019). Counterfactual Story Reasoning and Generation. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP).

PDF Code Dataset

(2019). WIQA: A dataset for "What if..." reasoning over procedural text. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP).

PDF Project Code Dataset

(2019). Be Consistent! Improving Procedural Text Comprehension using Label Consistency. Proceedings of the 17th Annual Meeting of the North American Association for Computational Linguistics (NAACL).

PDF Project Dataset

(2018). Discourse-Aware Neural Rewards for Coherent Text Generation. Proceedings of the 16th Annual Meeting of the North American Association for Computational Linguistics (NAACL).

PDF Dataset Poster

(2018). Deep Communicating Agents for Abstractive Summarization. Proceedings of the 16th Annual Meeting of the North American Association for Computational Linguistics (NAACL).

PDF Project Poster

(2018). Modeling Naive Psychology of Characters in Simple Commonsense Stories. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL).

PDF Project Code Dataset Slides

(2018). Learning to Write with Cooperative Discriminators. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL).

PDF Project Code Poster

(2016). Learning Prototypical Event Structure from Photo Albums. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL).

PDF Project Dataset Video

Experience

 
 
 
 
 

Student Researcher

Allen Institute for Artificial Intelligence (AI2)

Mar 2018 – Present Washington
 
 
 
 
 

Student Researcher

Microsoft Research

Nov 2017 – Dec 2018 Washington
 
 
 
 
 

Research Intern

Microsoft Research

Jun 2017 – Sep 2017 Washington
 
 
 
 
 

PhD Student

Paul G. Allen School of Computer Science and Engineering, University of Washington

Sep 2014 – Present Seattle, Washington