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.
SUPP.AI: finding evidence for supplement-drug interactions
Lucy Lu Wang | Oyvind Tafjord | Arman Cohan | Sarthak Jain | Sam Skjonsberg | Carissa Schoenick | Nick Botner | Waleed Ammar
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Dietary supplements are used by a large portion of the population, but information on their pharmacologic interactions is incomplete. To address this challenge, we present SUPP.AI, an application for browsing evidence of supplement-drug interactions (SDIs) extracted from the biomedical literature. We train a model to automatically extract supplement information and identify such interactions from the scientific literature. To address the lack of labeled data for SDI identification, we use labels of the closely related task of identifying drug-drug interactions (DDIs) for supervision. We fine-tune the contextualized word representations of the RoBERTa language model using labeled DDI data, and apply the fine-tuned model to identify supplement interactions. We extract 195k evidence sentences from 22M articles (P=0.82, R=0.58, F1=0.68) for 60k interactions. We create the SUPP.AI application for users to search evidence sentences extracted by our model. SUPP.AI is an attempt to close the information gap on dietary supplements by making up-to-date evidence on SDIs more discoverable for researchers, clinicians, and consumers. An informational video on how to use SUPP.AI is available at: https://youtu.be/dR0ucKdORwc
- Sam Skjonsberg 2
- Christopher Clark 1
- Jordi Salvador 1
- Dustin Schwenk 1
- Derrick Bonafilia 1
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