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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.

We present the benefits of eye-tracking features by evaluating the NER models on both individual datasets as well as in cross-domain settings.Improving this characterization would make ERPs a more useful tool for studying language comprehension.We take a step towards better understanding the ERPs by finetuning a language model to predict them.Recent work has shown that LSTMs trained on a generic language modeling objective capture syntax-sensitive generalizations such as long-distance number agreement.We have however no mechanistic understanding of how they accomplish this remarkable feat.

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