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A Balanced Data Approach for Evaluating Cross-Lingual Transfer: Mapping the Linguistic Blood Bank
Dan Malkin, Tomasz Limisiewicz, Gabriel Stanovsky
We show that the choice of pretraining languages affects downstream cross-lingual transfer for BERT-based models. We inspect zero-shot performance in balanced data conditions to mi…
Examining Cross-lingual Contextual Embeddings with Orthogonal Structural Probes
Tomasz Limisiewicz, David Mareček
State-of-the-art contextual embeddings are obtained from large language models available only for a few languages. For others, we need to learn representations using a multilingual…
Introducing Orthogonal Constraint in Structural Probes
Tomasz Limisiewicz, David Mareček
With the recent success of pre-trained models in NLP, a significant focus was put on interpreting their representations. One of the most prominent approaches is structural probing…
Gender Coreference and Bias Evaluation at WMT 2020
Tom Kocmi, Tomasz Limisiewicz, Gabriel Stanovsky
Gender bias in machine translation can manifest when choosing gender inflections based on spurious gender correlations. For example, always translating doctors as men and nurses as…
Syntax Representation in Word Embeddings and Neural Networks -- A Survey
Tomasz Limisiewicz, David Mareček
Neural networks trained on natural language processing tasks capture syntax even though it is not provided as a supervision signal. This indicates that syntactic analysis is essent…
Universal Dependencies according to BERT: both more specific and more general
Tomasz Limisiewicz, Rudolf Rosa, David Mareček
This work focuses on analyzing the form and extent of syntactic abstraction captured by BERT by extracting labeled dependency trees from self-attentions. Previous work showed that…