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Found 32 papers, 17 papers with code
A Cross-lingual Comparison of Human and Model Relative Word Importance
Relative word importance is a key metric for natural language processing.
CMCL 2021 Shared Task on Eye-Tracking Prediction
The goal of the task is to predict 5 different token- level eye-tracking metrics of the Zurich Cognitive Language Processing Corpus (ZuCo).
Interpreting Character Embeddings With Perceptual Representations: The Case of Shape, Sound, and Color
Character-level information is included in many NLP models, but evaluating the information encoded in character representations is an open issue.
CMCL 2022 Shared Task on Multilingual and Crosslingual Prediction of Human Reading Behavior
We present the second shared task on eye-tracking data prediction of the Cognitive Modeling and Computational Linguistics Workshop (CMCL).
Evaluating Webcam-based Gaze Data as an Alternative for Human Rationale Annotations
Rationales in the form of manually annotated input spans usually serve as ground truth when evaluating explainability methods in NLP.
Longer Fixations, More Computation: Gaze-Guided Recurrent Neural Networks
Humans read texts at a varying pace, while machine learning models treat each token in the same way in terms of a computational process.
WebQAmGaze: A Multilingual Webcam Eye-Tracking-While-Reading Dataset
We present WebQAmGaze, a multilingual low-cost eye-tracking-while-reading dataset, designed as the first webcam-based eye-tracking corpus of reading to support the development of explainable computational language processing models.
Cross-Lingual Transfer of Cognitive Processing Complexity
When humans read a text, their eye movements are influenced by the structural complexity of the input sentences.
Synthesizing Human Gaze Feedback for Improved NLP Performance
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).
Every word counts: A multilingual analysis of individual human alignment with model attention
Human fixation patterns have been shown to correlate strongly with Transformer-based attention.
The Copenhagen Corpus of Eye Tracking Recordings from Natural Reading of Danish Texts
Corpora of eye movements during reading of contextualized running text is a way of making such records available for natural language processing purposes.
Dynamic Human Evaluation for Relative Model Comparisons
Collecting human judgements is currently the most reliable evaluation method for natural language generation systems.
Reading Task Classification Using EEG and Eye-Tracking Data
The Zurich Cognitive Language Processing Corpus (ZuCo) provides eye-tracking and EEG signals from two reading paradigms, normal reading and task-specific reading.
Evaluating Bayes Error Estimators on Real-World Datasets with FeeBee
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.
Relative Importance in Sentence Processing
In neural language models, gradient-based saliency methods indicate the relative importance of a token for the target objective.
Multilingual Language Models Predict Human Reading Behavior
We analyze if large language models are able to predict patterns of human reading behavior.
Decoding EEG Brain Activity for Multi-Modal Natural Language Processing
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.
CogniVal in Action: An Interface for Customizable Cognitive Word Embedding Evaluation
We demonstrate the functionalities of the new user interface for CogniVal.
Human brain activity for machine attention
Cognitively inspired NLP leverages human-derived data to teach machines about language processing mechanisms.
Towards Best Practices for Leveraging Human Language Processing Signals for Natural Language Processing
NLP models are imperfect and lack intricate capabilities that humans access automatically when processing speech or reading a text.
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation
We introduce CGA, a conditional VAE architecture, to control, generate, and augment text.
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.
CogniVal: A Framework for Cognitive Word Embedding Evaluation
An interesting method of evaluating word representations is by how much they reflect the semantic representations in the human brain.
Advancing NLP with Cognitive Language Processing Signals
Cognitive language processing data such as eye-tracking features have shown improvements on single NLP tasks.
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 models.
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 good inductive bias on many attention functions in NLP.
Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks
In addition, a method to detect warning symptoms is implemented to render the classification task transparent from a medical perspective.
ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction
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.
Inconsistency Detection in Semantic Annotation
Automatically finding these inconsistencies and correcting them (even manually) can increase the quality of the data.
