activity
20172022
most citedNeural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence

10 citations · 40 across the 29 of their papers we have counts for

collaborators

48 papers

cs.CL20221 cited

StereoKG: Data-Driven Knowledge Graph Construction for Cultural Knowledge and Stereotypes

Awantee Deshpande, Dana Ruiter, Marius Mosbach +1

Analyzing ethnic or religious bias is important for improving fairness, accountability, and transparency of natural language processing models. However, many techniques rely on hum…

cs.CL20221 cited

Exploiting Social Media Content for Self-Supervised Style Transfer

Dana Ruiter, Thomas Kleinbauer, Cristina España-Bonet +2

Recent research on style transfer takes inspiration from unsupervised neural machine translation (UNMT), learning from large amounts of non-parallel data by exploiting cycle consis…

cs.CL2022

Placing M-Phasis on the Plurality of Hate: A Feature-Based Corpus of Hate Online

Dana Ruiter, Liane Reiners, Ashwin Geet D'Sa +6

Even though hate speech (HS) online has been an important object of research in the last decade, most HS-related corpora over-simplify the phenomenon of hate by attempting to label…

cs.CL2022

MCSE: Multimodal Contrastive Learning of Sentence Embeddings

Miaoran Zhang, Marius Mosbach, David Ifeoluwa Adelani +2

Learning semantically meaningful sentence embeddings is an open problem in natural language processing. In this work, we propose a sentence embedding learning approach that exploit…

cs.CL20223 cited

Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification

Dawei Zhu, Michael A. Hedderich, Fangzhou Zhai +2

Incorrect labels in training data occur when human annotators make mistakes or when the data is generated via weak or distant supervision. It has been shown that complex noise-hand…

cs.IR2022

Knowledge Base Index Compression via Dimensionality and Precision Reduction

Vilém Zouhar, Marius Mosbach, Miaoran Zhang +1

Recently neural network based approaches to knowledge-intensive NLP tasks, such as question answering, started to rely heavily on the combination of neural retrievers and readers.…