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To do so, we employ experimental methodologies which were originally developed in the field of psycholinguistics to study syntactic representation in the human mind.We examine neural network model behavior on sets of artificial sentences containing a variety of syntactically complex structures.In this work, we tackle the above challenges by introducing a new data sampling technique based on spaced repetition that dynamically samples informative and diverse unlabeled instances with respect to individual learner and instance characteristics.The proposed model is specifically effective in the context of neural models which can suffer from overfitting and high-variance gradients when trained with small amount of labeled data.
Through an ablation study, we find that the pre-trained encoder (acoustic model) accounts for most of the improvement, despite the fact that the shared language in these tasks is the target language text, not the source language audio.
These corpora were manually annotated with named entity labels.
Moreover, we show how gaze features, generalized on word type level, eliminate the need for recorded eye-tracking data at test time.
We find evidence for basic syntactic state representations in all models, but only the models trained on large datasets are sensitive to subtle lexical cues signaling changes in syntactic state.
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.