212 citations · 332 across the 15 of their papers we have counts for
5 papers · 1 filter
A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English
Marius Mosbach, Stefania Degaetano-Ortlieb, Marie-Pauline Krielke +2
Transformer-based language models achieve high performance on various tasks, but we still lack understanding of the kind of linguistic knowledge they learn and rely on. We evaluate…
On the Interplay Between Fine-tuning and Sentence-level Probing for Linguistic Knowledge in Pre-trained Transformers
Marius Mosbach, Anna Khokhlova, Michael A. Hedderich +1
Fine-tuning pre-trained contextualized embedding models has become an integral part of the NLP pipeline. At the same time, probing has emerged as a way to investigate the linguisti…
Fusion Models for Improved Visual Captioning
Marimuthu Kalimuthu, Aditya Mogadala, Marius Mosbach +1
Visual captioning aims to generate textual descriptions given images or videos. Traditionally, image captioning models are trained on human annotated datasets such as Flickr30k and…
Sparse Graph to Sequence Learning for Vision Conditioned Long Textual Sequence Generation
Aditya Mogadala, Marius Mosbach, Dietrich Klakow
Generating longer textual sequences when conditioned on the visual information is an interesting problem to explore. The challenge here proliferate over the standard vision conditi…
On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines
Marius Mosbach, Maksym Andriushchenko, Dietrich Klakow
Fine-tuning pre-trained transformer-based language models such as BERT has become a common practice dominating leaderboards across various NLP benchmarks. Despite the strong empiri…