Generating explainable answers to fact verification questions
Primary supervisor
Additional information
- Thorne, James, and Andreas Vlachos. "Automated Fact Checking: Task Formulations, Methods and Future Directions." Proceedings of the 27th International Conference on Computational Linguistics. 2018.
- Zhang, Wenxuan, et al. "AnswerFact: Fact Checking in Product Question Answering." Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.
- Jobanputra, Mayank. "Unsupervised Question Answering for Fact-Checking." Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.
- Liu, Shanshan, et al. "Neural machine reading comprehension: Methods and trends." Applied Sciences 9.18 (2019): 3698.
- Qiu, Boyu, et al. "A survey on neural machine reading comprehension." arXiv preprint arXiv:1906.03824 (2019).
- Kotonya, Neema, and Francesca Toni. "Explainable Automated Fact-Checking for Public Health Claims." Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.
Contact admissions office
Funding
- Competition Funded Project (Students Worldwide)
This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.
Project description
Online platforms such as social media and news sites have recently become vehicles for widespread misinformation, making it challenging for users to distinguish facts from hearsay. This can be addressed by the task of fact verification, which is aimed at automatically assessing the truthfulness of claims. Traditionally, fact verification has been cast as a binary classification task, whereby a given claim is labelled as true or false by a classifier (Thorne and Vlachos, 2018). More recently, it has also been formulated as a question answering task (Zhang et al., 2020, Jobanputra, 2019), thus providing end-users with the ability to pose their doubts as questions (e.g., "Does Ibuprofen make COVID-19 worse?") and retrieve answers in real-time. These answers can come in a short form (e.g., "No") or in a more informative form (e.g., "There is no evidence linking worse COVID-19 symptoms to Ibuprofen") that can include not only evidence from multiple sources but also explanations.
In this project, the PhD candidate will develop machine learning-based methods for fact verification, casting it as a question answering task. Along the way, the candidate will investigate neural architectures for machine reading comprehension (Liu et al., 2019, Qiu et al., 2019) as well as methods for information retrieval (for selecting relevant and reliable information sources) and natural language generation (for producing long-form answers to questions). Importantly, the candidate will propose approaches for making any generated answers explainable in the form of human-readable natural language text, enabling end-users to better understand and interpret the answers generated by the fact verification model (Kotonya and Toni, 2020).
Person specification
For information
- Candidates must hold a minimum of an upper Second Class UK Honours degree or international equivalent in a relevant science or engineering discipline.
- Candidates must meet the School's minimum English Language requirement.
- Candidates will be expected to comply with the University's policies and practices of equality, diversity and inclusion.
Essential
Applicants will be required to evidence the following skills and qualifications.
- You must be capable of performing at a very high level.
- You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.
Desirable
Applicants will be required to evidence the following skills and qualifications.
- You will have good time management.
- You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.
General
Applicants will be required to address the following.
- Comment on your transcript/predicted degree marks, outlining both strong and weak points.
- Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
- How well does your previous study prepare you for undertaking Postgraduate Research?
- Why do you believe you are suitable for doing Postgraduate Research?