Search Results for author: Michael A. Hedderich

Found 18 papers, 10 papers with code

A Piece of Theatre: Investigating How Teachers Design LLM Chatbots to Assist Adolescent Cyberbullying Education

no code implementations27 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.


Understanding and Mitigating Classification Errors Through Interpretable Token Patterns

no code implementations18 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.

Classification NER +1

Meta Self-Refinement for Robust Learning with Weak Supervision

1 code implementation15 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.

ANEA: Distant Supervision for Low-Resource Named Entity Recognition

1 code implementation25 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

Analysing the Noise Model Error for Realistic Noisy Label Data

3 code implementations24 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.

On the Interplay Between Fine-tuning and Sentence-level Probing for Linguistic Knowledge in Pre-trained Transformers

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.


Learning Functions to Study the Benefit of Multitask Learning

no code implementations9 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.

Mathematical Proofs Symbolic Regression

Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels

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

Using Multi-Sense Vector Embeddings for Reverse Dictionaries

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.

Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data

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.


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