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.- Anthology ID:
- P18-1028
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 295–305
- Language:
- URL:
- https://aclanthology.org/P18-1028
- DOI:
- 10.18653/v1/P18-1028
- Cite (ACL):
- Roy Schwartz, Sam Thomson, and Noah A. Smith. 2018. Bridging CNNs, RNNs, and Weighted Finite-State Machines. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 295–305, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Bridging CNNs, RNNs, and Weighted Finite-State Machines (Schwartz et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P18-1028.pdf
- Data
- SST