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Our model outperforms current semi-supervised learning approaches developed for neural networks on publicly-available datasets.We investigate the extent to which the behavior of neural network language models reflects incremental representations of syntactic state.We conclude that LSTMs are, to some extent, implementing genuinely syntactic processing mechanisms, paving the way to a more general understanding of grammatical encoding in LSTMs.Self-training is a semi-supervised learning approach for utilizing unlabeled data to create better learners.
Electroencephalography (EEG) recordings of brain activity taken while participants read or listen to language are widely used within the cognitive neuroscience and psycholinguistics communities as a tool to study language comprehension.
The gaze-augmented models for NER using token-level and type-level features outperform the baselines.
We present the benefits of eye-tracking features by evaluating the NER models on both individual datasets as well as in cross-domain settings.
This new approach to analysis shows for the first time that all of the ERPs are predictable from embeddings of a stream of language.
Prior work has only found two of the ERPs to be predictable.