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Structural Supervision Improves Learning of Non-Local Grammatical Dependencies by Wilcox, Ethan; Qian, Peng; Futrell, Richard; Ballesteros, Miguel; Levy, Roger is a scholarly article available to read on EtoBox.

What is Structural Supervision Improves Learning of Non-Local Grammatical Dependencies about?

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

Author

Wilcox, Ethan; Qian, Peng; Futrell, Richard; Ballesteros, Miguel; Levy, Roger

Published

2019

Language

EN