Dustin Wright

Dustin Wright

Postdoc at University of Copenhagen in the CopeNLU group. I study machine learning and natural language processing.

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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

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

NormCo: Deep Disease Normalization for Biomedical Knowledge Base Construction

Dustin Wright, Yannis Katsis, Raghav Mehta, Chun-Nan Hsu

Published in Automated Knowledge Base Construction, 2019

We develop a lightweight model for performing disease name normalization utilizing pretrained word-embeddings, distant supervision, and a dictionary of disease terms to outperform state of the art on disease name normalization on two datasets. AKBC 2019 Best Application Paper

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Claim Check-Worthiness Detection as Positive Unlabelled Learning

Dustin Wright and Isabelle Augenstein

Published in Findings of EMNLP, 2020

We show that when performing the task of claim check-worthiness detection, positive-unlabelled learning helps across multiple domains. Additionally, we highlight key similarities and differences in check-worthiness detection datasets.

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Generating Label Cohesive and Well-Formed Adversarial Claims

Pepa Atanasova* and Dustin Wright* and Isabelle Augenstein

Published in EMNLP, 2020

We propose a novel method using universal adversarial triggers and GPT-2 to generate difficult adversarial claims for fact checking models which preserve label direction and are semantically coherent, showing that such generated claims easily fool fact checking models.

Transformer Based Multi-Source Domain Adaptation

Dustin Wright and Isabelle Augenstein

Published in EMNLP, 2020

We demonstrate that when using large pretrained transformer models, mixture of experts methods can lead to significant gains in domain adaptation settings while domain adversarial training does not. We provide evidence that such models are relatively robust across domains, making homogenous predictions despite being fine-tuned on different domains.

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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

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