@inproceedings{clark-etal-2021-iconary,
title = "Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text",
author = "Clark, Christopher and
Salvador, Jordi and
Schwenk, Dustin and
Bonafilia, Derrick and
Yatskar, Mark and
Kolve, Eric and
Herrasti, Alvaro and
Choi, Jonghyun and
Mehta, Sachin and
Skjonsberg, Sam and
Schoenick, Carissa and
Sarnat, Aaron and
Hajishirzi, Hannaneh and
Kembhavi, Aniruddha and
Etzioni, Oren and
Farhadi, Ali",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.141/",
doi = "10.18653/v1/2021.emnlp-main.141",
pages = "1864--1886",
abstract = "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."
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text
%A Clark, Christopher
%A Salvador, Jordi
%A Schwenk, Dustin
%A Bonafilia, Derrick
%A Yatskar, Mark
%A Kolve, Eric
%A Herrasti, Alvaro
%A Choi, Jonghyun
%A Mehta, Sachin
%A Skjonsberg, Sam
%A Schoenick, Carissa
%A Sarnat, Aaron
%A Hajishirzi, Hannaneh
%A Kembhavi, Aniruddha
%A Etzioni, Oren
%A Farhadi, Ali
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F clark-etal-2021-iconary
%X 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.
%R 10.18653/v1/2021.emnlp-main.141
%U https://aclanthology.org/2021.emnlp-main.141/
%U https://doi.org/10.18653/v1/2021.emnlp-main.141
%P 1864-1886
Markdown (Informal)
[Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text](https://aclanthology.org/2021.emnlp-main.141/) (Clark et al., EMNLP 2021)
ACL
- 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, and Ali Farhadi. 2021. Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1864–1886, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.