Mitchell, J;
Saito Stenetorp, PLEPS;
Minervini, P;
Riedel, S;
(2018)
Extrapolation in NLP.
In:
Proceedings of the Workshop on Generalization in the Age of Deep Learning.
(pp. pp. 28-33).
Association for Computational Linguistics (ACL): New Orleans, LA, USA.
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Abstract
We argue that extrapolation to unseen data will often be easier for models that capture global structures, rather than just maximise their local fit to the training data. We show that this is true for two popular models: the Decomposable Attention Model and word2vec.
| Type: | Proceedings paper |
|---|---|
| Title: | Extrapolation in NLP |
| Event: | Proceedings of the Workshop on Generalization in the Age of Deep Learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.18653/v1/W18-1005 |
| Publisher version: | https://www.aclweb.org/anthology/W18-1005/ |
| Language: | English |
| Additional information: | This version is the version of record. 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/10051393 |
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