Minervini, P;
Bosnjak, M;
Rocktäschel, T;
Riedel, S;
Grefenstette, E;
(2020)
Differentiable Reasoning on Large Knowledge Bases and Natural Language.
In:
Proceedings of The Thirty-fourth AAAI Conference on Artificial Intelligence The Thirty-second Innovative Applications Of Artificial Intelligence Conference, The Tenth AAAI Symposium on Educational Advances In Artificial Intelligence.
(pp. pp. 5182-5190).
AAAI Press: New York, NY, USA.
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Abstract
Reasoning with knowledge expressed in natural language and Knowledge Bases (KBs) is a major challenge for Artificial Intelligence, with applications in machine reading, dialogue, and question answering. General neural architectures that jointly learn representations and transformations of text are very datainefficient, and it is hard to analyse their reasoning process. These issues are addressed by end-to-end differentiable reasoning systems such as Neural Theorem Provers (NTPs), although they can only be used with small-scale symbolic KBs. In this paper we first propose Greedy NTPs (GNTPs), an extension to NTPs addressing their complexity and scalability limitations, thus making them applicable to real-world datasets. This result is achieved by dynamically constructing the computation graph of NTPs and including only the most promising proof paths during inference, thus obtaining orders of magnitude more efficient models 1 . Then, we propose a novel approach for jointly reasoning over KBs and textual mentions, by embedding logic facts and natural language sentences in a shared embedding space. We show that GNTPs perform on par with NTPs at a fraction of their cost while achieving competitive link prediction results on large datasets, providing explanations for predictions, and inducing interpretable models.
| Type: | Proceedings paper |
|---|---|
| Title: | Differentiable Reasoning on Large Knowledge Bases and Natural Language |
| Event: | The Thirty-fourth AAAI Conference on Artificial Intelligence The Thirty-second Innovative Applications Of Artificial Intelligence Conference, The Tenth AAAI Symposium on Educational Advances In Artificial Intelligence. |
| ISBN-13: | 978-1-57735-823-7 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://www.aaai.org/Library/AAAI/aaai20contents.p... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10117328 |
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