Finding Convincing Arguments Using Scalable Bayesian Preference Learning
- Submitting institution
-
University of Bristol
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 225062775
- Type
- D - Journal article
- DOI
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10.1162/tacl_a_00026
- Title of journal
- Transactions of the Association for Computational Linguistics
- Article number
- -
- First page
- 357
- Volume
- 6
- Issue
- -
- ISSN
- 2307-387X
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
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A - Artificial Intelligence and Autonomy
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposed a new approach to modelling the persuasiveness of arguments. It is the first to introduce a highly scalable variational inference method to Gaussian process preference learning and has inspired multiple papers that apply this methodology to solve a range of ambiguous labelling problems in natural language, including papers in EMNLP 2018 (https://www.aclweb.org/anthology/D18-1445/), ACL 2019 and related workshops (https://www.aclweb.org/anthology/P19-1572/, https://www.aclweb.org/anthology/W19-4024.pdf ), and Machine Learning (https://doi.org/10.1007/s10994-019-05867-2). The open source code has also attracted interest (https://github.com/ukplab/tacl2018-preference-convincing).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -