1 code implementation • 13 Mar 2025 • Florian Eichin, Yang Janet Liu, Barbara Plank, Michael A. Hedderich
Discourse understanding is essential for many NLP tasks, yet most existing work remains constrained by framework-dependent discourse representations.
1 code implementation • 28 Oct 2024 • Florian Eichin, Carolin M. Schuster, Georg Groh, Michael A. Hedderich
Topic modeling is a key method in text analysis, but existing approaches are limited by assuming one topic per document or fail to scale efficiently for large, noisy datasets of short texts.
1 code implementation • 23 Oct 2024 • Qiqi Chen, Xinpeng Wang, Philipp Mondorf, Michael A. Hedderich, Barbara Plank
In this paper, we analyze the roles of the generator and discriminator separately to better understand the conditions when ToT is beneficial.
no code implementations • 16 Jun 2024 • Bolei Ma, Xinpeng Wang, Tiancheng Hu, Anna-Carolina Haensch, Michael A. Hedderich, Barbara Plank, Frauke Kreuter
This paper aims to bridge this gap by providing a comprehensive overview of recent works on the evaluation of AOVs in LLMs.
no code implementations • 27 Feb 2024 • Michael A. Hedderich, Natalie N. Bazarova, Wenting Zou, Ryun Shim, Xinda Ma, Qian Yang
In offering this tool, we explore teachers' distinctive needs when designing chatbots to assist their teaching, and how chatbot design tools might better support them.
no code implementations • 18 Nov 2023 • Michael A. Hedderich, Jonas Fischer, Dietrich Klakow, Jilles Vreeken
Characterizing these errors in easily interpretable terms gives insight into whether a classifier is prone to making systematic errors, but also gives a way to act and improve the classifier.
no code implementations • 3 Jun 2022 • Dawei Zhu, Michael A. Hedderich, Fangzhou Zhai, David Ifeoluwa Adelani, Dietrich Klakow
However, text classification in low-resource languages is still challenging due to the lack of annotated data.
1 code implementation • 15 May 2022 • Dawei Zhu, Xiaoyu Shen, Michael A. Hedderich, Dietrich Klakow
Training deep neural networks (DNNs) under weak supervision has attracted increasing research attention as it can significantly reduce the annotation cost.
1 code implementation • NAACL 2022 • Miaoran Zhang, Marius Mosbach, David Ifeoluwa Adelani, Michael A. Hedderich, Dietrich Klakow
Learning semantically meaningful sentence embeddings is an open problem in natural language processing.
1 code implementation • insights (ACL) 2022 • Dawei Zhu, Michael A. Hedderich, Fangzhou Zhai, David Ifeoluwa Adelani, Dietrich Klakow
Incorrect labels in training data occur when human annotators make mistakes or when the data is generated via weak or distant supervision.
no code implementations • 8 Jul 2021 • Michael A. Hedderich, Benjamin Roth, Katharina Kann, Barbara Plank, Alex Ratner, Dietrich Klakow
Welcome to WeaSuL 2021, the First Workshop on Weakly Supervised Learning, co-located with ICLR 2021.
1 code implementation • 25 Feb 2021 • Michael A. Hedderich, Lukas Lange, Dietrich Klakow
Distant supervision allows obtaining labeled training corpora for low-resource settings where only limited hand-annotated data exists.
Low Resource Named Entity Recognition
named-entity-recognition
+2
3 code implementations • 24 Jan 2021 • 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.
1 code implementation • NAACL 2021 • Michael A. Hedderich, Lukas Lange, Heike Adel, Jannik Strötgen, Dietrich Klakow
Deep neural networks and huge language models are becoming omnipresent in natural language applications.
1 code implementation • EMNLP 2020 • Michael A. Hedderich, David Adelani, Dawei Zhu, Jesujoba Alabi, Udia Markus, Dietrich Klakow
Multilingual transformer models like mBERT and XLM-RoBERTa have obtained great improvements for many NLP tasks on a variety of languages.
no code implementations • EMNLP (BlackboxNLP) 2020 • Marius Mosbach, Anna Khokhlova, Michael A. Hedderich, Dietrich Klakow
Our analysis reveals that while fine-tuning indeed changes the representations of a pre-trained model and these changes are typically larger for higher layers, only in very few cases, fine-tuning has a positive effect on probing accuracy that is larger than just using the pre-trained model with a strong pooling method.
no code implementations • 9 Jun 2020 • Gabriele Bettgenhäuser, Michael A. Hedderich, Dietrich Klakow
Although multitask learning has achieved improved performance in some problems, there are also tasks that lose performance when trained together.
no code implementations • 18 Mar 2020 • David Ifeoluwa Adelani, Michael A. Hedderich, Dawei Zhu, Esther van den Berg, Dietrich Klakow
Techniques such as distant and weak supervision can be used to create labeled data in a (semi-) automatic way.
Low Resource Named Entity Recognition
named-entity-recognition
+3
1 code implementation • IJCNLP 2019 • Lukas Lange, Michael A. Hedderich, Dietrich Klakow
In low-resource settings, the performance of supervised labeling models can be improved with automatically annotated or distantly supervised data, which is cheap to create but often noisy.
Low Resource Named Entity Recognition
named-entity-recognition
+4
1 code implementation • WS 2019 • Michael A. Hedderich, Andrew Yates, Dietrich Klakow, Gerard de Melo
However, they typically cannot serve as a drop-in replacement for conventional single-sense embeddings, because the correct sense vector needs to be selected for each word.
no code implementations • NAACL 2019 • Debjit Paul, Mittul Singh, Michael A. Hedderich, Dietrich Klakow
In our experiments on Chunking and NER, this approach performs more robustly than the baselines.
1 code implementation • WS 2018 • Michael A. Hedderich, Dietrich Klakow
Manually labeled corpora are expensive to create and often not available for low-resource languages or domains.