187 citations · 475 across the 25 of their papers we have counts for
5 papers · 1 filter
Multimodal Pretraining Unmasked: A Meta-Analysis and a Unified Framework of Vision-and-Language BERTs
Emanuele Bugliarello, Ryan Cotterell, Naoaki Okazaki +1
Large-scale pretraining and task-specific fine-tuning is now the standard methodology for many tasks in computer vision and natural language processing. Recently, a multitude of me…
Multimodal Speech Recognition with Unstructured Audio Masking
Tejas Srinivasan, Ramon Sanabria, Florian Metze +1
Visual context has been shown to be useful for automatic speech recognition (ASR) systems when the speech signal is noisy or corrupted. Previous work, however, has only demonstrate…
Fine-Grained Grounding for Multimodal Speech Recognition
Tejas Srinivasan, Ramon Sanabria, Florian Metze +1
Multimodal automatic speech recognition systems integrate information from images to improve speech recognition quality, by grounding the speech in the visual context. While visual…
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…
The Sensitivity of Language Models and Humans to Winograd Schema Perturbations
Mostafa Abdou, Vinit Ravishankar, Maria Barrett +3
Large-scale pretrained language models are the major driving force behind recent improvements in performance on the Winograd Schema Challenge, a widely employed test of common sens…