Will Radford


2020

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Proceedings of the Second Workshop on Gender Bias in Natural Language Processing
Marta R. Costa-jussà | Christian Hardmeier | Will Radford | Kellie Webster
Proceedings of the Second Workshop on Gender Bias in Natural Language Processing

2019

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Proceedings of the First Workshop on Gender Bias in Natural Language Processing
Marta R. Costa-jussà | Christian Hardmeier | Will Radford | Kellie Webster
Proceedings of the First Workshop on Gender Bias in Natural Language Processing

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Gendered Ambiguous Pronoun (GAP) Shared Task at the Gender Bias in NLP Workshop 2019
Kellie Webster | Marta R. Costa-jussà | Christian Hardmeier | Will Radford
Proceedings of the First Workshop on Gender Bias in Natural Language Processing

The 1st ACL workshop on Gender Bias in Natural Language Processing included a shared task on gendered ambiguous pronoun (GAP) resolution. This task was based on the coreference challenge defined in Webster et al. (2018), designed to benchmark the ability of systems to resolve pronouns in real-world contexts in a gender-fair way. 263 teams competed via a Kaggle competition, with the winning system achieving logloss of 0.13667 and near gender parity. We review the approaches of eleven systems with accepted description papers, noting their effective use of BERT (Devlin et al., 2018), both via fine-tuning and for feature extraction, as well as ensembling.

2018

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Can adult mental health be predicted by childhood future-self narratives? Insights from the CLPsych 2018 Shared Task
Kylie Radford | Louise Lavrencic | Ruth Peters | Kim Kiely | Ben Hachey | Scott Nowson | Will Radford
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

The CLPsych 2018 Shared Task B explores how childhood essays can predict psychological distress throughout the author’s life. Our main aim was to build tools to help our psychologists understand the data, propose features and interpret predictions. We submitted two linear regression models: ModelA uses simple demographic and word-count features, while ModelB uses linguistic, entity, typographic, expert-gazetteer, and readability features. Our models perform best at younger prediction ages, with our best unofficial score at 23 of 0.426 disattenuated Pearson correlation. This task is challenging and although predictive performance is limited, we propose that tight integration of expertise across computational linguistics and clinical psychology is a productive direction.

2017

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Learning to generate one-sentence biographies from Wikidata
Andrew Chisholm | Will Radford | Ben Hachey
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We investigate the generation of one-sentence Wikipedia biographies from facts derived from Wikidata slot-value pairs. We train a recurrent neural network sequence-to-sequence model with attention to select facts and generate textual summaries. Our model incorporates a novel secondary objective that helps ensure it generates sentences that contain the input facts. The model achieves a BLEU score of 41, improving significantly upon the vanilla sequence-to-sequence model and scoring roughly twice that of a simple template baseline. Human preference evaluation suggests the model is nearly as good as the Wikipedia reference. Manual analysis explores content selection, suggesting the model can trade the ability to infer knowledge against the risk of hallucinating incorrect information.

2016

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Classification of mental health forum posts
Glen Pink | Will Radford | Ben Hachey
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology

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Discovering Entity Knowledge Bases on the Web
Andrew Chisholm | Will Radford | Ben Hachey
Proceedings of the 5th Workshop on Automated Knowledge Base Construction

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:telephone::person::sailboat::whale::okhand: ; or “Call me Ishmael” – How do you translate emoji?
Will Radford | Ben Hachey | Bo Han | Andy Chisholm
Proceedings of the Australasian Language Technology Association Workshop 2016

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Presenting a New Dataset for the Timeline Generation Problem
Xavier Holt | Will Radford | Ben Hachey
Proceedings of the Australasian Language Technology Association Workshop 2016

2015

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Named entity recognition with document-specific KB tag gazetteers
Will Radford | Xavier Carreras | James Henderson
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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Cheap and easy entity evaluation
Ben Hachey | Joel Nothman | Will Radford
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Joint Apposition Extraction with Syntactic and Semantic Constraints
Will Radford | James R. Curran
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2010

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Tracking Information Flow between Primary and Secondary News Sources
Will Radford | Ben Hachey | James Curran | Maria Milosavljevic
Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media

2009

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Tracking Information Flow in Financial Text
Will Radford | Ben Hachey | James R. Curran | Maria Milosavljevic
Proceedings of the Australasian Language Technology Association Workshop 2009

2007

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TAT: An Author Profiling Tool with Application to Arabic Emails
Dominique Estival | Tanja Gaustad | Son Bao Pham | Will Radford | Ben Hutchinson
Proceedings of the Australasian Language Technology Workshop 2007