Michael A. Hedderich
Michael A. Hedderich
Cornell University
Bestätigte E-Mail-Adresse bei - Startseite
Zitiert von
Zitiert von
A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios
MA Hedderich, L Lange, H Adel, J Strötgen, D Klakow
arXiv preprint arXiv:2010.12309, 2020
Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages
MA Hedderich, D Adelani, D Zhu, J Alabi, U Markus, D Klakow
Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020
Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data
MA Hedderich, D Klakow
Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP …, 2018
On the Interplay Between Fine-tuning and Sentence-level Probing for Linguistic Knowledge in Pre-trained Transformers
M Mosbach, A Khokhlova, MA Hedderich, D Klakow
Findings of the Association for Computational Linguistics: EMNLP 2020, 2502–2516, 2020
Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labeling
D Paul, M Singh, MA Hedderich, D Klakow
Proceedings of the 2019 Conference of the North American Chapter of the …, 2019
Using Multi-Sense Vector Embeddings for Reverse Dictionaries
MA Hedderich, A Yates, D Klakow, G de Melo
Proceedings of the 13th International Conference on Computational Semantics …, 2019
Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels
L Lange, MA Hedderich, D Klakow
Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019
MCSE: Multimodal Contrastive Learning of Sentence Embeddings
M Zhang, M Mosbach, DI Adelani, MA Hedderich, D Klakow
arXiv preprint arXiv:2204.10931, 2022
ANEA: Distant Supervision for Low-Resource Named Entity Recognition
MA Hedderich, L Lange, D Klakow
ICLR 2021 Workshop Practical Machine Learning For Developing Countries, 2021
Distant Supervision and Noisy Label Learning for Low Resource Named Entity Recognition: A Study on Hausa and Yorùbá
D Ifeoluwa Adelani, MA Hedderich, D Zhu, E van den Berg, D Klakow
ICLR 2020 Workshop Pratical Machine Learning for Developing Countries Workshop, 2020
SoloFinger: Robust Microgestures while Grasping Everyday Objects
A Sharma, MA Hedderich, D Bhardwaj, B Fruchard, J McIntosh, AS Nittala, ...
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems …, 2021
Analysing the Noise Model Error for Realistic Noisy Label Data
MA Hedderich, D Zhu, D Klakow
Proceedings of the AAAI Conference on Artificial Intelligence 35 (9), 7675-7684, 2021
Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification
D Zhu, MA Hedderich, F Zhai, DI Adelani, D Klakow
arXiv preprint arXiv:2204.09371, 2022
Learning Functions to Study the Benefit of Multitask Learning
G Bettgenhäuser, MA Hedderich, D Klakow
arXiv preprint arXiv:2006.05561, 2020
Chatbots Facilitating Consensus-Building in Asynchronous Co-Design
J Shin, MA Hedderich, AS Lucero, A Oulasvirta
Proceedings of the 35th Annual ACM Symposium on User Interface Software and …, 2022
Meta Self-Refinement for Robust Learning with Weak Supervision
D Zhu, X Shen, MA Hedderich, D Klakow
arXiv preprint arXiv:2205.07290, 2022
Label-Descriptive Patterns and Their Application to Characterizing Classification Errors
MA Hedderich, J Fischer, D Klakow, J Vreeken
International Conference on Machine Learning, 8691-8707, 2022
SparseIMU: Computational Design of Sparse IMU Layouts for Sensing Fine-Grained Finger Microgestures
A Sharma, C Salchow-Hömmen, VS Mollyn, AS Nittala, MA Hedderich, ...
ACM Transactions on Computer-Human Interaction, 2022
Task-Adaptive Pre-Training for Boosting Learning With Noisy Labels: A Study on Text Classification for African Languages
D Zhu, MA Hedderich, F Zhai, DI Adelani, D Klakow
arXiv preprint arXiv:2206.01476, 2022
Weak supervision and label noise handling for natural language processing in low-resource scenarios
MA Hedderich
Saarländische Universitäts-und Landesbibliothek, 2022
Das System kann den Vorgang jetzt nicht ausführen. Versuchen Sie es später erneut.
Artikel 1–20