70 citations · 118 across the 10 of their papers we have counts for
11 papers
VISaGE: Understanding Visual Generics and Exceptions
Stella Frank, Emily Allaway
While Vision Language Models (VLMs) learn conceptual representations, in the form of generalized knowledge, during training, they are typically used to analyze individual instances…
Multilingual Multimodal Learning with Machine Translated Text
Chen Qiu, Dan Oneata, Emanuele Bugliarello +2
Most vision-and-language pretraining research focuses on English tasks. However, the creation of multilingual multimodal evaluation datasets (e.g. Multi30K, xGQA, XVNLI, and MaRVL)…
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…
Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color
Mostafa Abdou, Artur Kulmizev, Daniel Hershcovich +3
Pretrained language models have been shown to encode relational information, such as the relations between entities or concepts in knowledge-bases -- (Paris, Capital, France). Howe…
Vision-and-Language or Vision-for-Language? On Cross-Modal Influence in Multimodal Transformers
Stella Frank, Emanuele Bugliarello, Desmond Elliott
Pretrained vision-and-language BERTs aim to learn representations that combine information from both modalities. We propose a diagnostic method based on cross-modal input ablation…
CompGuessWhat?!: A Multi-task Evaluation Framework for Grounded Language Learning
Alessandro Suglia, Ioannis Konstas, Andrea Vanzo +4
Approaches to Grounded Language Learning typically focus on a single task-based final performance measure that may not depend on desirable properties of the learned hidden represen…