Neuro-Symbolic Artificial Intelligence for Efficient and Interpretable Natural Language Understanding

   Department of Computer Science

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  Dr Harish Tayyar Madabushi, Prof Özgür Şimşek  No more applications being accepted  Competition Funded PhD Project (UK Students Only)

About the Project

The University of Bath is inviting applications for the following PhD project commencing in October 2023.

The significant, and by some measures superhuman [1], performance of deep neural models has led to their wide adoption across multiple fields including Natural Language Processing. This adoption has come at the cost of the decades of progress made in what is now referred to as “traditional” Natural Language Processing. In particular, deep learning completely ignores the significant body of research into what is called symbolic AI, such as explicit knowledge representation and reasoning. Additionally, this dependence on deep learning has led to methods which are fundamentally opaque [2]. Popular methods of explaining the decisions made by deep neural models are often not faithful: that is, the explanation generated by a model is not what the model itself is using to arrive at that decision.

To address these shortcomings, this project focuses on the area which spans the intersection of neural and symbolic AI: a fast growing and increasingly important field known as neuro-symbolic artificial intelligence [3].

Neuro-symbolic methods have the potential of benefiting from the advantages of both deep neural models (i.e., performance) and symbolic methods (i.e., transparency and mutability) - see also [9]. Such methods would focus on the development of methods that incorporate declarative knowledge into deep neural methods, including the use of knowledge representation logics, such as natural logic. For example, [8] use a sequence to sequence model to generate natural logic based inferences as proofs, thus providing an inherently interpretable model for fact verification. Similarly, [11] propose a method of infusing knowledge directly into pre-trained language models by enabling them to directly access information pertaining to entities mentioned in text. Other work in this regard includes that by [10] who explore methods of incorporating mutable knowledge into models.

In addition to the development of neuro-symbolic models which are inherently explainable and transparent, this project requires the application of these methods on social media data. The recent rise in hate, abuse, and fake news in online discourse [3, 4, 5, 6, 7] has made research into effective and interpretable methods essential.

Project keywords: Neuro-symbolic AI, Natural Language Processing, NLP, Natural Language Understanding, NLU, Interpretable NLP, Expandability, Social Media Analysis, Fake news, Online hate.

Candidate Requirements:

Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree (or the equivalent). A master’s level qualification would also be advantageous.

Non-UK applicants must meet our English language entry requirement.

Enquiries and Applications:

Informal enquiries are strongly encouraged and should be directed to Dr Harish Tayyar Madabushi ([Email Address Removed]).

Formal applications should be submitted via the University of Bath’s online application form for a PhD in Computer Science prior to the application deadline of Sunday 22 January 2023.

More information about applying for a PhD at Bath may be found on our website.

Funding Eligibility:

To be eligible for funding, you must qualify as a Home student. The eligibility criteria for Home fee status are detailed and too complex to be summarised here in full; however, as a general guide, the following applicants will normally qualify subject to meeting residency requirements: UK and Irish nationals (living in the UK or EEA/Switzerland), those with Indefinite Leave to Remain and EU nationals with pre-settled or settled status in the UK under the EU Settlement Scheme. This is not intended to be an exhaustive list. Additional information may be found on our fee status guidance webpage, on the GOV.UK website and on the UKCISA website.

Exceptional Overseas students (e.g. with a UK Master’s Distinction or international equivalent and relevant research experience), who are interested in this project, should contact the lead supervisor in the first instance to discuss the possibility of applying for supplementary funding.

Equality, Diversity and Inclusion:

We value a diverse research environment and aim to be an inclusive university, where difference is celebrated and respected. We welcome and encourage applications from under-represented groups.

If you have circumstances that you feel we should be aware of that have affected your educational attainment, then please feel free to tell us about it in your application form. The best way to do this is a short paragraph at the end of your personal statement.

Computer Science (8)

Funding Notes

A studentship includes Home tuition fees, a stipend (£17,668 per annum, 2022/23 rate) and research/training expenses (£1,000 per annum) for up to 3.5 years. Eligibility criteria apply – see Funding Eligibility section above.


[1] Zoph, B., Bello, I., Kumar, S., Du, N., Huang, Y., Dean, J., Shazeer, N. and Fedus, W., 2022. Designing effective sparse expert models. arXiv preprint arXiv:2202.08906.
[2] Wiegreffe, S., Marasović, A. and Smith, N.A., 2021, November. Measuring Association Between Labels and Free-Text Rationales. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 10266-10284).
[3] Ha, L., Andreu Perez, L., & Ray, R. (2021). Mapping Recent Development in Scholarship on Fake News and Misinformation, 2008 to 2017: Disciplinary Contribution, Topics, and Impact. American Behavioral Scientist, 65(2), 290–315.
[4] Persily, N., & Tucker, J. A. (2020). Social Media and Democracy: The State of the Field, Prospects for Reform. Cambridge University Press.
[5] Alyukov, M. (2022). Propaganda, authoritarianism and Russia’s invasion of Ukraine. Nature Human Behaviour, 6(6), Article 6.
[6] Carson, A., & Wright, S. (2022). Fake news and democracy: Definitions, impact and response. Australian Journal of Political Science, 0(0), 1–10.
[7] Lanius, C., Weber, R., & MacKenzie, W. I. (2021). Use of bot and content flags to limit the spread of misinformation among social networks: A behavior and attitude survey. Social Network Analysis and Mining, 11(1), 32.
[8] Krishna, A., Riedel, S. and Vlachos, A., 2022. Proofver: Natural logic theorem proving for fact verification. Transactions of the Association for Computational Linguistics, 10, pp.1013-1030.
[9] Tayyar Madabushi, Ramisch, Idiart, Villavicencio. COLING Tutorial: Psychological, Cognitive and Linguistic BERTology (Part 1), COLING
[10] Verga, P., Sun, H., Soares, L.B. and Cohen, W., 2021, June. Adaptable and interpretable neural memory over symbolic knowledge. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 3678-3691).
[11] Févry, T., Soares, L.B., Fitzgerald, N., Choi, E. and Kwiatkowski, T., 2020, November. Entities as Experts: Sparse Memory Access with Entity Supervision. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 4937-4951).

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