Aaron Sarnat


2021

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Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text
Christopher Clark | Jordi Salvador | Dustin Schwenk | Derrick Bonafilia | Mark Yatskar | Eric Kolve | Alvaro Herrasti | Jonghyun Choi | Sachin Mehta | Sam Skjonsberg | Carissa Schoenick | Aaron Sarnat | Hannaneh Hajishirzi | Aniruddha Kembhavi | Oren Etzioni | Ali Farhadi
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Communicating with humans is challenging for AIs because it requires a shared understanding of the world, complex semantics (e.g., metaphors or analogies), and at times multi-modal gestures (e.g., pointing with a finger, or an arrow in a diagram). We investigate these challenges in the context of Iconary, a collaborative game of drawing and guessing based on Pictionary, that poses a novel challenge for the research community. In Iconary, a Guesser tries to identify a phrase that a Drawer is drawing by composing icons, and the Drawer iteratively revises the drawing to help the Guesser in response. This back-and-forth often uses canonical scenes, visual metaphor, or icon compositions to express challenging words, making it an ideal test for mixing language and visual/symbolic communication in AI. We propose models to play Iconary and train them on over 55,000 games between human players. Our models are skillful players and are able to employ world knowledge in language models to play with words unseen during training.

2017

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Interactive Visualization for Linguistic Structure
Aaron Sarnat | Vidur Joshi | Cristian Petrescu-Prahova | Alvaro Herrasti | Brandon Stilson | Mark Hopkins
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We provide a visualization library and web interface for interactively exploring a parse tree or a forest of parses. The library is not tied to any particular linguistic representation, but provides a general-purpose API for the interactive exploration of hierarchical linguistic structure. To facilitate rapid understanding of a complex structure, the API offers several important features, including expand/collapse functionality, positional and color cues, explicit visual support for sequential structure, and dynamic highlighting to convey node-to-text correspondence.