34 citations · 111 across the 25 of their papers we have counts for
31 papers
Train Once, Use Flexibly: A Modular Framework for Multi-Aspect Neural News Recommendation
Andreea Iana, Goran Glavaš, Heiko Paulheim
Recent neural news recommenders (NNRs) extend content-based recommendation (1) by aligning additional aspects (e.g., topic, sentiment) between candidate news and user history or (2…
Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language Representations
Gregor Geigle, Radu Timofte, Goran Glavaš
Vision-and-language (VL) models with separate encoders for each modality (e.g., CLIP) have become the go-to models for zero-shot image classification and image-text retrieval. They…
A General-Purpose Multilingual Document Encoder
Onur Galoğlu, Robert Litschko, Goran Glavaš
Massively multilingual pretrained transformers (MMTs) have tremendously pushed the state of the art on multilingual NLP and cross-lingual transfer of NLP models in particular. Whil…
Simplifying Content-Based Neural News Recommendation: On User Modeling and Training Objectives
Andreea Iana, Goran Glavaš, Heiko Paulheim
The advent of personalized news recommendation has given rise to increasingly complex recommender architectures. Most neural news recommenders rely on user click behavior and typic…
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…
Can Demographic Factors Improve Text Classification? Revisiting Demographic Adaptation in the Age of Transformers
Chia-Chien Hung, Anne Lauscher, Dirk Hovy +2
Demographic factors (e.g., gender or age) shape our language. Previous work showed that incorporating demographic factors can consistently improve performance for various NLP tasks…