Abstract
While Recurrent Neural Networks (RNNs) are famously known to be Turing complete, this relies on infinite precision in the states and unbounded computation time. We consider the case of RNNs with finite precision whose computation time is linear in the input length. Under these limitations, we show that different RNN variants have different computational power. In particular, we show that the LSTM and the Elman-RNN with ReLU activation are strictly stronger than the RNN with a squashing activation and the GRU. This is achieved because LSTMs and ReLU-RNNs can easily implement counting behavior. We show empirically that the LSTM does indeed learn to effectively use the counting mechanism.
| 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 (Short Papers) |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 740-745 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781948087346 |
| DOIs | |
| State | Published - 2018 |
| 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) |
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| Volume | 2 |
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
Funding Information:The research leading to the results presented in this paper is supported by the European Union’s Seventh Framework Programme (FP7) under grant agreement no. 615688 (PRIME), The Israeli Science Foundation (grant number 1555/15), and The Allen Institute for Artificial Intelligence.
Publisher Copyright:
© 2018 Association for Computational Linguistics