The goal of the task is to predict 5 different token- level eye-tracking metrics of the Zurich Cognitive Language Processing Corpus (ZuCo).
We present the second shared task on eye-tracking data prediction of the Cognitive Modeling and Computational Linguistics Workshop (CMCL).
Character-level information is included in many NLP models, but evaluating the information encoded in character representations is an open issue.
Humans read texts at a varying pace, while machine learning models treat each token in the same way in terms of a computational process.
We create WebQAmGaze, a multilingual low-cost eye-tracking-while-reading dataset, designed to support the development of fair and transparent NLP models.
When humans read a text, their eye movements are influenced by the structural complexity of the input sentences.
Feedback can be either explicit (e. g. ranking used in training language models) or implicit (e. g. using human cognitive signals in the form of eyetracking).
Corpora of eye movements during reading of contextualized running text is a way of making such records available for natural language processing purposes.
The Zurich Cognitive Language Processing Corpus (ZuCo) provides eye-tracking and EEG signals from two reading paradigms, normal reading and task-specific reading.
The Bayes error rate (BER) is a fundamental concept in machine learning that quantifies the best possible accuracy any classifier can achieve on a fixed probability distribution.
In this paper, we present the first large-scale study of systematically analyzing the potential of EEG brain activity data for improving natural language processing tasks, with a special focus on which features of the signal are most beneficial.
NLP models are imperfect and lack intricate capabilities that humans access automatically when processing speech or reading a text.
We introduce CGA, a conditional VAE architecture, to control, generate, and augment text.
We recorded and preprocessed ZuCo 2. 0, a new dataset of simultaneous eye-tracking and electroencephalography during natural reading and during annotation.
An interesting method of evaluating word representations is by how much they reflect the semantic representations in the human brain.
Cognitive language processing data such as eye-tracking features have shown improvements on single NLP tasks.
Previous research shows that eye-tracking data contains information about the lexical and syntactic properties of text, which can be used to improve natural language processing models.
Learning attention functions requires large volumes of data, but many NLP tasks simulate human behavior, and in this paper, we show that human attention really does provide a good inductive bias on many attention functions in NLP.
In addition, a method to detect warning symptoms is implemented to render the classification task transparent from a medical perspective.
Reliably detecting relevant relations between entities in unstructured text is a valuable resource for knowledge extraction, which is why it has awaken significant interest in the field of Natural Language Processing.