Structural Supervision Improves Learning of Non-Local Grammatical Dependencies

Structural Supervision Improves Learning of Non-Local Grammatical Dependencies. Wilcox, E., Qian, P., Futrell, R., Ballesteros, M., & Levy, R. In Proceedings of North American Association for Computational Linguistics (NAACL), pages 3302–3312, 2019.

Paper abstract bibtex

State-of-the-art LSTM language models trained on large corpora learn sequential contingencies in impressive detail and have been shown to acquire a number of non-local grammatical dependencies with some success. Here we investigate whether supervision with hierarchical structure enhances learning of a range of grammatical dependencies, a question that has previously been addressed only for subject-verb agreement. Using controlled experimental methods from psycholinguistics, we compare the performance of word-based LSTM models versus two models that represent hierarchical structure and deploy it in left-to-right processing: Recurrent Neural Network Grammars (RNNGs) (Dyer et al., 2016) and a incrementalized version of the Parsing-as-Language-Modeling configuration from Chariak et al., (2016). Models are tested on a diverse range of configurations for two classes of non-local grammatical dependencies in English—Negative Polarity licensing and Filler–Gap Dependencies. Using the same training data across models, we find that structurally-supervised models outperform the LSTM, with the RNNG demonstrating best results on both types of grammatical dependencies and even learning many of the Island Constraints on the filler–gap dependency. Structural supervision thus provides data efficiency advantages over purely string-based training of neural language models in acquiring human-like generalizations about non-local grammatical dependencies.

@inproceedings{Wilcox2019,
abstract = {State-of-the-art LSTM language models trained on large corpora learn sequential contingencies in impressive detail and have been shown to acquire a number of non-local grammatical dependencies with some success. Here we investigate whether supervision with hierarchical structure enhances learning of a range of grammatical dependencies, a question that has previously been addressed only for subject-verb agreement. Using controlled experimental methods from psycholinguistics, we compare the performance of word-based LSTM models versus two models that represent hierarchical structure and deploy it in left-to-right processing: Recurrent Neural Network Grammars (RNNGs) (Dyer et al., 2016) and a incrementalized version of the Parsing-as-Language-Modeling configuration from Chariak et al., (2016). Models are tested on a diverse range of configurations for two classes of non-local grammatical dependencies in English---Negative Polarity licensing and Filler--Gap Dependencies. Using the same training data across models, we find that structurally-supervised models outperform the LSTM, with the RNNG demonstrating best results on both types of grammatical dependencies and even learning many of the Island Constraints on the filler--gap dependency. Structural supervision thus provides data efficiency advantages over purely string-based training of neural language models in acquiring human-like generalizations about non-local grammatical dependencies.},
archivePrefix = {arXiv},
arxivId = {1903.00943},
author = {Wilcox, Ethan and Qian, Peng and Futrell, Richard and Ballesteros, Miguel and Levy, Roger},
booktitle = {Proceedings of North American Association for Computational Linguistics (NAACL)},
eprint = {1903.00943},
file = {:Users/shanest/Documents/Library/Wilcox et al/Proceedings of North American Association for Computational Linguistics (NAACL)/Wilcox et al. - 2019 - Structural Supervision Improves Learning of Non-Local Grammatical Dependencies.pdf:pdf},
keywords = {method: psycholinguistic,method: structural supervision,phenomenon: NPIs,phenomenon: island constraints},
pages = {3302--3312},
title = {{Structural Supervision Improves Learning of Non-Local Grammatical Dependencies}},
url = {http://arxiv.org/abs/1903.00943},
year = {2019}
}

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