12 citations · 15 across the 6 of their papers we have counts for
6 papers
Domain-Specific Word Embeddings with Structure Prediction
Stephanie Brandl, David Lassner, Anne Baillot +1
Complementary to finding good general word embeddings, an important question for representation learning is to find dynamic word embeddings, e.g., across time or domain. Current me…
Every word counts: A multilingual analysis of individual human alignment with model attention
Stephanie Brandl, Nora Hollenstein
Human fixation patterns have been shown to correlate strongly with Transformer-based attention. Those correlation analyses are usually carried out without taking into account indiv…
How Conservative are Language Models? Adapting to the Introduction of Gender-Neutral Pronouns
Stephanie Brandl, Ruixiang Cui, Anders Søgaard
Gender-neutral pronouns have recently been introduced in many languages to a) include non-binary people and b) as a generic singular. Recent results from psycholinguistics suggest…
Do Transformer Models Show Similar Attention Patterns to Task-Specific Human Gaze?
Stephanie Brandl, Oliver Eberle, Jonas Pilot +1
Learned self-attention functions in state-of-the-art NLP models often correlate with human attention. We investigate whether self-attention in large-scale pre-trained language mode…
Challenges and Strategies in Cross-Cultural NLP
Daniel Hershcovich, Stella Frank, Heather Lent +11
Various efforts in the Natural Language Processing (NLP) community have been made to accommodate linguistic diversity and serve speakers of many different languages. However, it is…
Balancing the composition of word embeddings across heterogenous data sets
Stephanie Brandl, David Lassner, Maximilian Alber
Word embeddings capture semantic relationships based on contextual information and are the basis for a wide variety of natural language processing applications. Notably these relat…