[HTML][HTML] Attention in psychology, neuroscience, and machine learning
GW Lindsay - Frontiers in computational neuroscience, 2020 - frontiersin.org
Attention is the important ability to flexibly control limited computational resources. It has
been studied in conjunction with many other topics in neuroscience and psychology …
been studied in conjunction with many other topics in neuroscience and psychology …
ZuCo, a simultaneous EEG and eye-tracking resource for natural sentence reading
Abstract We present the Zurich Cognitive Language Processing Corpus (ZuCo), a dataset
combining electroencephalography (EEG) and eye-tracking recordings from subjects …
combining electroencephalography (EEG) and eye-tracking recordings from subjects …
Sequence classification with human attention
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 …
human behavior, and in this paper, we show that human attention really does provide a …
Sequence labelling and sequence classification with gaze: Novel uses of eye‐tracking data for Natural Language Processing
Eye‐tracking data from reading provide a structured signal with a fine‐grained temporal
resolution which closely follows the sequential structure of the text. It is highly correlated with …
resolution which closely follows the sequential structure of the text. It is highly correlated with …
Benchmarking and explaining large language model-based code generation: A causality-centric approach
While code generation has been widely used in various software development scenarios,
the quality of the generated code is not guaranteed. This has been a particular concern in …
the quality of the generated code is not guaranteed. This has been a particular concern in …
ZuCo 2.0: A dataset of physiological recordings during natural reading and annotation
We recorded and preprocessed ZuCo 2.0, a new dataset of simultaneous eye-tracking and
electroencephalography during natural reading and during annotation. This corpus contains …
electroencephalography during natural reading and during annotation. This corpus contains …
Multilingual language models predict human reading behavior
We analyze if large language models are able to predict patterns of human reading
behavior. We compare the performance of language-specific and multilingual pretrained …
behavior. We compare the performance of language-specific and multilingual pretrained …
CMCL 2021 shared task on eye-tracking prediction
Eye-tracking data from reading represent an important resource for both linguistics and
natural language processing. The ability to accurately model gaze features is crucial to …
natural language processing. The ability to accurately model gaze features is crucial to …
Entity recognition at first sight: Improving NER with eye movement information
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 …
syntactic properties of text, which can be used to improve natural language processing …
An efficient framework for sentence similarity modeling
Sentence similarity modeling lies at the core of many natural language processing
applications, and thus has received much attention. Owing to the success of word …
applications, and thus has received much attention. Owing to the success of word …