b'Anne Lauscher'

AI Chat

Featured Co-authors

Iryna Gurevych
161 publications
Ivan Vulić
87 publications
Arman Cohan
60 publications
Anna Korhonen
52 publications
Steffen Eger
48 publications
Goran Glavaš
48 publications
Philipp Mayr
48 publications
Silvio Peroni
41 publications
Kyle Lo
40 publications
Simone Paolo Ponzetto
38 publications
Federico Bianchi
34 publications

research

∙ 09/13/2023

How (Not) to Use Sociodemographic Information for Subjective NLP Tasks

Annotators' sociodemographic backgrounds (i.e., the individual compositi...

0 Tilman Beck, et al. ∙

research

∙ 05/26/2023

Stereotypes and Smut: The (Mis)representation of Non-cisgender Identities by Text-to-Image Models

Cutting-edge image generation has been praised for producing high-qualit...

0 Eddie L. Ungless, et al. ∙

research

∙ 05/25/2023

What about em? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns

As 3rd-person pronoun usage shifts to include novel forms, e.g., neopron...

1 Anne Lauscher, et al. ∙

research

∙ 11/08/2022

SocioProbe: What, When, and Where Language Models Learn about Sociodemographics

Pre-trained language models (PLMs) have outperformed other NLP models on...

0 Anne Lauscher, et al. ∙

research

∙ 11/08/2022

Bridging Fairness and Environmental Sustainability in Natural Language Processing

Fairness and environmental impact are important research directions for ...

0 Marius Hessenthaler, et al. ∙

research

∙ 10/13/2022

Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformers

Demographic factors (e.g., gender or age) shape our language. Previous w...

0 Chia-Chien Hung, et al. ∙

research

∙ 10/12/2022

Back to the Future: On Potential Histories in NLP

Machine learning and NLP require the construction of datasets to train a...

0 Zeerak Talat, et al. ∙

research

∙ 08/01/2022

On the Limitations of Sociodemographic Adaptation with Transformers

Sociodemographic factors (e.g., gender or age) shape our language. Previ...

0 Chia-Chien Hung, et al. ∙

research

∙ 05/20/2022

Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog

Research on (multi-domain) task-oriented dialog (TOD) has predominantly ...

0 Chia-Chien Hung, et al. ∙

research

∙ 04/08/2022

Fair and Argumentative Language Modeling for Computational Argumentation

Although much work in NLP has focused on measuring and mitigating stereo...

0 Carolin Holtermann, et al. ∙

research

∙ 02/24/2022

Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender

The world of pronouns is changing. From a closed class of words with few...

0 Anne Lauscher, et al. ∙

research

∙ 10/15/2021

DS-TOD: Efficient Domain Specialization for Task Oriented Dialog

Recent work has shown that self-supervised dialog-specific pretraining o...

0 Chia-Chien Hung, et al. ∙

research

∙ 09/08/2021

Sustainable Modular Debiasing of Language Models

Unfair stereotypical biases (e.g., gender, racial, or religious biases) ...

0 Anne Lauscher, et al. ∙

research

∙ 08/13/2021

Diachronic Analysis of German Parliamentary Proceedings: Ideological Shifts through the Lens of Political Biases

We analyze bias in historical corpora as encoded in diachronic distribut...

0 Tobias Walter, et al. ∙

research

∙ 07/01/2021

MultiCite: Modeling realistic citations requires moving beyond the single-sentence single-label setting

Citation context analysis (CCA) is an important task in natural language...

3 Anne Lauscher, et al. ∙

research

∙ 07/01/2021

Scientia Potentia Est – On the Role of Knowledge in Computational Argumentation

Despite extensive research in the past years, the computational modeling...

0 Anne Lauscher, et al. ∙

research

∙ 06/07/2021

RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models

Text representation models are prone to exhibit a range of societal bias...

0 Soumya Barikeri, et al. ∙

research

∙ 03/11/2021

DebIE: A Platform for Implicit and Explicit Debiasing of Word Embedding Spaces

Recent research efforts in NLP have demonstrated that distributional wor...

0 Niklas Friedrich, et al. ∙

research

∙ 11/03/2020

Creating a Domain-diverse Corpus for Theory-based Argument Quality Assessment

Computational models of argument quality (AQ) have focused primarily on ...

0 Lily Ng, et al. ∙

research

∙ 11/03/2020

AraWEAT: Multidimensional Analysis of Biases in Arabic Word Embeddings

Recent work has shown that distributional word vector spaces often encod...

0 Anne Lauscher, et al. ∙

research

∙ 06/01/2020

Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing

Argumentative quality is an important feature of everyday writing in man...

0 Anne Lauscher, et al. ∙

research

∙ 05/25/2020

The OpenCitations Data Model

A variety of schemas and ontologies are currently used for the machine-r...

0 Marilena Daquino, et al. ∙

research

∙ 05/24/2020

Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers

Following the major success of neural language models (LMs) such as BERT...

0 Anne Lauscher, et al. ∙

research

∙ 05/01/2020

From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers

Massively multilingual transformers pretrained with language modeling ob...

0 Anne Lauscher, et al. ∙

research

∙ 09/13/2019

A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spaces

Distributional word vectors have recently been shown to encode many of t...

9 Anne Lauscher, et al. ∙

research

∙ 09/05/2019

Informing Unsupervised Pretraining with External Linguistic Knowledge

Unsupervised pretraining models have been shown to facilitate a wide ran...

0 Anne Lauscher, et al. ∙

research

∙ 04/26/2019

Are We Consistently Biased? Multidimensional Analysis of Biases in Distributional Word Vectors

Word embeddings have recently been shown to reflect many of the pronounc...

0 Anne Lauscher, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world