Assessing the Unitary RNN as an End-to-End Compositional Model of Syntax
View/ Open
Editors
Moortgat, M
Wijnholds, G
Volume
366
Pagination
9 - 22 (14)
Publisher
DOI
10.4204/EPTCS.366.4
Issue
ISSN
2075-2180
Metadata
Show full item recordAbstract
We show that both an LSTM and a unitary-evolution recurrent neural network (URN) can achieve encouraging accuracy on two types of syntactic patterns: context-free long distance agreement, and mildly context-sensitive cross serial dependencies. This work extends recent experiments on deeply nested context-free long distance dependencies, with similar results. URNs differ from LSTMs in that they avoid non-linear activation functions, and they apply matrix multiplication to word embeddings encoded as unitary matrices. This permits them to retain all information in the processing of an input string over arbitrary distances. It also causes them to satisfy strict compositionality. URNs constitute a significant advance in the search for explainable models in deep learning applied to NLP.
Authors
Bernardy, J-P; Lappin, S; End-to-End Compositional Models of Vector-Based Semantics, 2022 (E2ECOMPVEC)Collections
Licence information
Copyright statements
The following license files are associated with this item: