Search Results for author: Maria Barrett

Found 22 papers, 7 papers with code

DaNLP: An open-source toolkit for Danish Natural Language Processing

no code implementations NoDaLiDa 2021 Amalie Brogaard Pauli, Maria Barrett, Ophélie Lacroix, Rasmus Hvingelby

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

no code implementations CRAC (ACL) 2021 Maria Barrett, Hieu Lam, Martin Wu, Ophélie Lacroix, Barbara Plank, Anders Søgaard

Automatic coreference resolution is understudied in Danish even though most of the Danish Dependency Treebank (Buch-Kromann, 2003) is annotated with coreference relations.

coreference-resolution Entity Disambiguation +2

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

no code implementations LREC 2022 Nora Hollenstein, Maria Barrett, Marina Björnsdóttir

Corpora of eye movements during reading of contextualized running text is a way of making such records available for natural language processing purposes.

Spurious Correlations in Cross-Topic Argument Mining

1 code implementation Joint Conference on Lexical and Computational Semantics 2021 Terne Sasha Thorn Jakobsen, Maria Barrett, Anders S{\o}gaard

Recent work in cross-topic argument mining attempts to learn models that generalise across topics rather than merely relying on within-topic spurious correlations.

Argument Mining Topic Models

Decoding EEG Brain Activity for Multi-Modal Natural Language Processing

no code implementations17 Feb 2021 Nora Hollenstein, Cedric Renggli, Benjamin Glaus, Maria Barrett, Marius Troendle, Nicolas Langer, Ce Zhang

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.

BIG-bench Machine Learning EEG +3

Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias

1 code implementation EMNLP 2020 Ana Valeria Gonzalez, Maria Barrett, Rasmus Hvingelby, Kellie Webster, Anders Søgaard

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.


Human brain activity for machine attention

1 code implementation9 Jun 2020 Lukas Muttenthaler, Nora Hollenstein, Maria Barrett

Cognitively inspired NLP leverages human-derived data to teach machines about language processing mechanisms.

Dimensionality Reduction EEG +2

The Sensitivity of Language Models and Humans to Winograd Schema Perturbations

2 code implementations ACL 2020 Mostafa Abdou, Vinit Ravishankar, Maria Barrett, Yonatan Belinkov, Desmond Elliott, Anders Søgaard

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.

Common Sense Reasoning

Adversarial Removal of Demographic Attributes Revisited

no code implementations IJCNLP 2019 Maria Barrett, Yova Kementchedjhieva, Yanai Elazar, Desmond Elliott, Anders S{\o}gaard

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.

Sequence Classification with Human Attention

1 code implementation CONLL 2018 Maria Barrett, Joachim Bingel, Nora Hollenstein, Marek Rei, Anders S{\o}gaard

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.

Abusive Language Classification +4

Predicting misreadings from gaze in children with reading difficulties

no code implementations WS 2018 Joachim Bingel, Maria Barrett, Sigrid Klerke

We present the first work on predicting reading mistakes in children with reading difficulties based on eye-tracking data from real-world reading teaching.

Multi-Task Learning Reading Comprehension +1

Cross-lingual Transfer of Correlations between Parts of Speech and Gaze Features

no code implementations COLING 2016 Maria Barrett, Frank Keller, Anders S{\o}gaard

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.

Cross-Lingual Transfer POS +1

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