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Your search keyword '"Choi, Yejin"' showing total 15 results
Start Over Author "Choi, Yejin" Journal proceedings of the aaai conference on artificial intelligence
15 results on '"Choi, Yejin"'

12. Learning Interpretable Spatial Operations in a Rich 3D Blocks World

Author

Bisk, Yonatan, Shih, Kevin J., Choi, Yejin, and Marcu, Daniel

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, General Medicine, Computation and Language (cs.CL)
Abstract

In this paper, we study the problem of mapping natural language instructions to complex spatial actions in a 3D blocks world. We first introduce a new dataset that pairs complex 3D spatial operations to rich natural language descriptions that require complex spatial and pragmatic interpretations such as "mirroring", "twisting", and "balancing". This dataset, built on the simulation environment of Bisk, Yuret, and Marcu (2016), attains language that is significantly richer and more complex, while also doubling the size of the original dataset in the 2D environment with 100 new world configurations and 250,000 tokens. In addition, we propose a new neural architecture that achieves competitive results while automatically discovering an inventory of interpretable spatial operations (Figure 5), AAAI 2018

Published
2018

14. Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects

Author

Bagherinezhad, Hessam, Hajishirzi, Hannaneh, Choi, Yejin, and Farhadi, Ali

Subjects
FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, General Medicine
Abstract

Human vision greatly benefits from the information about sizes of objects. The role of size in several visual reasoning tasks has been thoroughly explored in human perception and cognition. However, the impact of the information about sizes of objects is yet to be determined in AI. We postulate that this is mainly attributed to the lack of a comprehensive repository of size information. In this paper, we introduce a method to automatically infer object sizes, leveraging visual and textual information from web. By maximizing the joint likelihood of textual and visual observations, our method learns reliable relative size estimates, with no explicit human supervision. We introduce the relative size dataset and show that our method outperforms competitive textual and visual baselines in reasoning about size comparisons., To appear in AAAI 2016

Published
2016

15. Refer-to-as Relations as Semantic Knowledge

Author

Feng, Song, primary, Ravi, Sujith, additional, Kumar, Ravi, additional, Kuznetsova, Polina, additional, Liu, Wei, additional, Berg, Alexander, additional, Berg, Tamara, additional, and Choi, Yejin, additional

Published
2015
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