51 citations · 138 across the 9 of their papers we have counts for
14 papers
Synthesizing Human Gaze Feedback for Improved NLP Performance
Varun Khurana, Yaman Kumar Singla, Nora Hollenstein +2
Integrating human feedback in models can improve the performance of natural language processing (NLP) models. Feedback can be either explicit (e.g. ranking used in training languag…
Every word counts: A multilingual analysis of individual human alignment with model attention
Stephanie Brandl, Nora Hollenstein
Human fixation patterns have been shown to correlate strongly with Transformer-based attention. Those correlation analyses are usually carried out without taking into account indiv…
Dynamic Human Evaluation for Relative Model Comparisons
Thórhildur Thorleiksdóttir, Cedric Renggli, Nora Hollenstein +1
Collecting human judgements is currently the most reliable evaluation method for natural language generation systems. Automatic metrics have reported flaws when applied to measure…
Relative Importance in Sentence Processing
Nora Hollenstein, Lisa Beinborn
Determining the relative importance of the elements in a sentence is a key factor for effortless natural language understanding. For human language processing, we can approximate p…
Multilingual Language Models Predict Human Reading Behavior
Nora Hollenstein, Federico Pirovano, Ce Zhang +2
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 transformer…
Decoding EEG Brain Activity for Multi-Modal Natural Language Processing
Nora Hollenstein, Cedric Renggli, Benjamin Glaus +4
Until recently, human behavioral data from reading has mainly been of interest to researchers to understand human cognition. However, these human language processing signals can al…
Human brain activity for machine attention
Lukas Muttenthaler, Nora Hollenstein, Maria Barrett
Cognitively inspired NLP leverages human-derived data to teach machines about language processing mechanisms. Recently, neural networks have been augmented with behavioral data to…
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation
Giuseppe Russo, Nora Hollenstein, Claudiu Musat +1
We introduce CGA, a conditional VAE architecture, to control, generate, and augment text. CGA is able to generate natural English sentences controlling multiple semantic and syntac…
ZuCo 2.0: A Dataset of Physiological Recordings During Natural Reading and Annotation
Nora Hollenstein, Marius Troendle, Ce Zhang +1
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 gaze…
CogniVal: A Framework for Cognitive Word Embedding Evaluation
Nora Hollenstein, Antonio de la Torre, Nicolas Langer +1
An interesting method of evaluating word representations is by how much they reflect the semantic representations in the human brain. However, most, if not all, previous works only…
Advancing NLP with Cognitive Language Processing Signals
Nora Hollenstein, Maria Barrett, Marius Troendle +3
When we read, our brain processes language and generates cognitive processing data such as gaze patterns and brain activity. These signals can be recorded while reading. Cognitive…
Entity Recognition at First Sight: Improving NER with Eye Movement Information
Nora Hollenstein, Ce Zhang
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 mod…
Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks
Ivan Girardi, Pengfei Ji, An-phi Nguyen +5
We present an operational component of a real-world patient triage system. Given a specific patient presentation, the system is able to assess the level of medical urgency and issu…
ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction
Jonathan Rotsztejn, Nora Hollenstein, Ce Zhang
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…