34 citations · 74 across the 9 of their papers we have counts for
18 papers · 1 filter
The Devil is in the Details: On Models and Training Regimes for Few-Shot Intent Classification
Mohsen Mesgar, Thy Thy Tran, Goran Glavas +1
Few-shot Intent Classification (FSIC) is one of the key challenges in modular task-oriented dialog systems. While advanced FSIC methods are similar in using pretrained language mod…
Sustainable Modular Debiasing of Language Models
Anne Lauscher, Tobias Lüken, Goran Glavaš
Unfair stereotypical biases (e.g., gender, racial, or religious biases) encoded in modern pretrained language models (PLMs) have negative ethical implications for widespread adopti…
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models
Soumya Barikeri, Anne Lauscher, Ivan Vulić +1
Text representation models are prone to exhibit a range of societal biases, reflecting the non-controlled and biased nature of the underlying pretraining data, which consequently l…
Orthogonal Language and Task Adapters in Zero-Shot Cross-Lingual Transfer
Marko Vidoni, Ivan Vulić, Goran Glavaš
Adapter modules, additional trainable parameters that enable efficient fine-tuning of pretrained transformers, have recently been used for language specialization of multilingual t…
AraWEAT: Multidimensional Analysis of Biases in Arabic Word Embeddings
Anne Lauscher, Rafik Takieddin, Simone Paolo Ponzetto +1
Recent work has shown that distributional word vector spaces often encode human biases like sexism or racism. In this work, we conduct an extensive analysis of biases in Arabic wor…
Probing Pretrained Language Models for Lexical Semantics
Ivan Vulić, Edoardo Maria Ponti, Robert Litschko +2
The success of large pretrained language models (LMs) such as BERT and RoBERTa has sparked interest in probing their representations, in order to unveil what types of knowledge the…