Search Results for author:
Found 23 papers, 8 papers with code
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.
Advancing NLP with Cognitive Language Processing Signals
Cognitive language processing data such as eye-tracking features have shown improvements on single NLP tasks.
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.
The Sensitivity of Language Models and Humans to Winograd Schema Perturbations
Large-scale pretrained language models are the major driving force behind recent improvements in performance on the Winograd Schema Challenge, a widely employed test of common sense reasoning ability.
Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias
The one-sided focus on English in previous studies of gender bias in NLP misses out on opportunities in other languages: English challenge datasets such as GAP and WinoGender highlight model preferences that are "hallucinatory", e. g., disambiguating gender-ambiguous occurrences of 'doctor' as male doctors.
Spurious Correlations in Cross-Topic Argument Mining
Recent work in cross-topic argument mining attempts to learn models that generalise across topics rather than merely relying on within-topic spurious correlations.
The Sensitivity of Annotator Bias to Task Definitions in Argument Mining
NLP models are dependent on the data they are trained on, including how this data is annotated.
Predicting misreadings from gaze in children with reading difficulties
We present the first work on predicting reading mistakes in children with reading difficulties based on eye-tracking data from real-world reading teaching.
Cross-lingual Transfer of Correlations between Parts of Speech and Gaze Features
Several recent studies have shown that eye movements during reading provide information about grammatical and syntactic processing, which can assist the induction of NLP models.
Adversarial Removal of Demographic Attributes Revisited
Elazar and Goldberg (2018) showed that protected attributes can be extracted from the representations of a debiased neural network for mention detection at above-chance levels, by evaluating a diagnostic classifier on a held-out subsample of the data it was trained on.
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.
DaNE: A Named Entity Resource for Danish
We present a named entity annotation for the Danish Universal Dependencies treebank using the CoNLL-2003 annotation scheme: DaNE.
Human brain activity for machine attention
Cognitively inspired NLP leverages human-derived data to teach machines about language processing mechanisms.
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.
DaNLP: An open-source toolkit for Danish Natural Language Processing
We present an open-source toolkit for Danish Natural Language Processing, enabling easy access to Danish NLP’s latest advancements.
Resources and Evaluations for Danish Entity Resolution
Automatic coreference resolution is understudied in Danish even though most of the Danish Dependency Treebank (Buch-Kromann, 2003) is annotated with coreference relations.
Can Humans Identify Domains?
Textual domain is a crucial property within the Natural Language Processing (NLP) community due to its effects on downstream model performance.
