Abstract
Recurrent and convolutional neural networks comprise two distinct families of models that have proven to be useful for encoding natural language utterances. In this paper we present SoPa, a new model that aims to bridge these two approaches. SoPa combines neural representation learning with weighted finite-state automata (WFSAs) to learn a soft version of traditional surface patterns. We show that SoPa is an extension of a one-layer CNN, and that such CNNs are equivalent to a restricted version of SoPa, and accordingly, to a restricted form of WFSA. Empirically, on three text classification tasks, SoPa is comparable or better than both a BiLSTM (RNN) baseline and a CNN baseline, and is particularly useful in small data settings.
| Original language | English |
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| Title of host publication | ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers) |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 295-305 |
| Number of pages | 11 |
| ISBN (Electronic) | 9781948087322 |
| State | Published - 2018 |
| Externally published | Yes |
| Event | 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 - Melbourne, Australia Duration: 15 Jul 2018 → 20 Jul 2018 |
Publication series
| Name | ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers) |
|---|---|
| Volume | 1 |
Conference
| Conference | 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 |
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| Country/Territory | Australia |
| City | Melbourne |
| Period | 15/07/18 → 20/07/18 |
Bibliographical note
Publisher Copyright:© 2018 Association for Computational Linguistics