Nora Hollenstein | DeepAI

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∙ 03/31/2023

WebQAmGaze: A Multilingual Webcam Eye-Tracking-While-Reading Dataset

We create WebQAmGaze, a multilingual low-cost eye-tracking-while-reading...

0 Tiago Ribeiro, et al. ∙

∙ 02/24/2023

Cross-Lingual Transfer of Cognitive Processing Complexity

When humans read a text, their eye movements are influenced by the struc...

0 Charlotte Pouw, et al. ∙

∙ 02/11/2023

Synthesizing Human Gaze Feedback for Improved NLP Performance

Integrating human feedback in models can improve the performance of natu...

10 Varun Khurana, et al. ∙

∙ 10/05/2022

Every word counts: A multilingual analysis of individual human alignment with model attention

Human fixation patterns have been shown to correlate strongly with Trans...

0 Stephanie Brandl, et al. ∙

∙ 04/28/2022

The Copenhagen Corpus of Eye Tracking Recordings from Natural Reading of Danish Texts

Eye movement recordings from reading are one of the richest signals of h...

0 Nora Hollenstein, et al. ∙

∙ 12/15/2021

Dynamic Human Evaluation for Relative Model Comparisons

Collecting human judgements is currently the most reliable evaluation me...

0 Þórhildur Þorleiksdóttir, et al. ∙

∙ 12/12/2021

Reading Task Classification Using EEG and Eye-Tracking Data

The Zurich Cognitive Language Processing Corpus (ZuCo) provides eye-trac...

0 Nora Hollenstein, et al. ∙

∙ 08/30/2021

Evaluating Bayes Error Estimators on Read-World Datasets with FeeBee

The Bayes error rate (BER) is a fundamental concept in machine learning ...

0 Cedric Renggli, et al. ∙

∙ 06/07/2021

Relative Importance in Sentence Processing

Determining the relative importance of the elements in a sentence is a k...

0 Nora Hollenstein, et al. ∙

∙ 04/12/2021

Multilingual Language Models Predict Human Reading Behavior

We analyze if large language models are able to predict patterns of huma...

0 Nora Hollenstein, et al. ∙

∙ 02/17/2021

Decoding EEG Brain Activity for Multi-Modal Natural Language Processing

Until recently, human behavioral data from reading has mainly been of in...

0 Nora Hollenstein, et al. ∙

∙ 10/16/2020

On Automatic Feasibility Study for Machine Learning Application Development with ease.ml/snoopy

In our experience working with domain experts who are using today's Auto...

0 Cedric Renggli, et al. ∙

∙ 06/09/2020

Human brain activity for machine attention

Cognitively inspired NLP leverages human-derived data to teach machines ...

0 Lukas Muttenthaler, et al. ∙

∙ 04/30/2020

Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation

In this work, we present a text generation approach with multi-attribute...

0 Giuseppe Russo, et al. ∙

∙ 12/02/2019

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...

0 Nora Hollenstein, et al. ∙

∙ 09/19/2019

CogniVal: A Framework for Cognitive Word Embedding Evaluation

An interesting method of evaluating word representations is by how much ...

0 Nora Hollenstein, et al. ∙

∙ 04/04/2019

Advancing NLP with Cognitive Language Processing Signals

When we read, our brain processes language and generates cognitive proce...

0 Nora Hollenstein, et al. ∙

∙ 02/26/2019

Entity Recognition at First Sight: Improving NER with Eye Movement Information

Previous research shows that eye-tracking data contains information abou...

0 Nora Hollenstein, et al. ∙

∙ 09/28/2018

Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks

We present an operational component of a real-world patient triage syste...

0 Ivan Girardi, et al. ∙

∙ 04/05/2018

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 t...

0 Jonathan Rotsztejn, et al. ∙

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