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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Yufei Wang
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Posts
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publications
Data61-csiro systems at the clpsych 2016 shared task
Published in Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology, 2016
Authors: Sunghwan Mac Kim, Yufei Wang, Stephen Wan, Cécile Paris
Parsing speech with Transformer.
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The Role of Features and Context on Suicide Ideation Detection
Published in Proceedings of the Australasian Language Technology Association Workshop 2016, 2016
Authors: Yufei Wang, Stephen Wan, Cécile Paris
Suicide Ideation
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Extracting Definitions and Hypernyms with a Two-Phase Framework
Published in International Conference on Database Systems for Advanced Applications, 2019
Authors: Yifang Sun, Shifeng Liu, Yufei Wang, Wei Wang
Definitions and Hypernyms
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Neural Constituency Parsing of Speech Transcripts
Published in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 2019
Authors: Paria Jamshid Lou, Yufei Wang, Mark Johnson
Parsing speech with Transformer.
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How to best use Syntax in Semantic Role Labelling
Published in 57th Annual Meeting of the Association for Computational Linguistics, 2019
Authors: Yufei Wang, Mark Johnson, Stephen Wan, Yifang Sun, Wei Wang
We evaluate three different ways of encoding syntactic parses and three different ways of injecting them into a state-of-the-art neural ELMo-based SRL sequence labelling model.
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nocaps: novel object captioning at scale
Published in International Conference on Computer Vision (ICCV), 2019
Authors: Harsh Agrawal*, Karan Desai*, Yufei Wang, Xinlei Chen, Rishabh Jain, Mark Johnson, Dhruv Batra, Devi Parikh, Stefan Lee, Peter Anderson
To encourage the development of image captioning models that can learn visual concepts from alternative data sources, such as object detection datasets, we present the first large-scale benchmark for this task. Dubbed nocaps, for novel object captioning at scale, our benchmark consists of 166,100 human-generated captions describing 15,100 images from the Open Images validation and test sets.
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