3 citations · 6 across the 3 of their papers we have counts for
6 papers
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…
Analysing the Noise Model Error for Realistic Noisy Label Data
Michael A. Hedderich, Dawei Zhu, Dietrich Klakow
Distant and weak supervision allow to obtain large amounts of labeled training data quickly and cheaply, but these automatic annotations tend to contain a high amount of errors. A…
Neural Data-to-Text Generation with LM-based Text Augmentation
Ernie Chang, Xiaoyu Shen, Dawei Zhu +2
For many new application domains for data-to-text generation, the main obstacle in training neural models consists of a lack of training data. While usually large numbers of instan…
Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages
Michael A. Hedderich, David Adelani, Dawei Zhu +3
Multilingual transformer models like mBERT and XLM-RoBERTa have obtained great improvements for many NLP tasks on a variety of languages. However, recent works also showed that res…
Distant Supervision and Noisy Label Learning for Low Resource Named Entity Recognition: A Study on Hausa and Yorùbá
David Ifeoluwa Adelani, Michael A. Hedderich, Dawei Zhu +2
The lack of labeled training data has limited the development of natural language processing tools, such as named entity recognition, for many languages spoken in developing countr…
Image Manipulation with Natural Language using Two-sidedAttentive Conditional Generative Adversarial Network
Dawei Zhu, Aditya Mogadala, Dietrich Klakow
Altering the content of an image with photo editing tools is a tedious task for an inexperienced user. Especially, when modifying the visual attributes of a specific object in an i…