212 citations · 741 across the 81 of their papers we have counts for
14 papers · 1 filter
CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain
Lukas Lange, Heike Adel, Jannik Strötgen +1
The field of natural language processing (NLP) has recently seen a large change towards using pre-trained language models for solving almost any task. Despite showing great improve…
Enhancing Reinforcement Learning with discrete interfaces to learn the Dyck Language
Florian Dietz, Dietrich Klakow
Even though most interfaces in the real world are discrete, no efficient way exists to train neural networks to make use of them, yet. We enhance an Interaction Network (a Reinforc…
Label-Descriptive Patterns and Their Application to Characterizing Classification Errors
Michael Hedderich, Jonas Fischer, Dietrich Klakow +1
State-of-the-art deep learning methods achieve human-like performance on many tasks, but make errors nevertheless. Characterizing these errors in easily interpretable terms gives i…
How Familiar Does That Sound? Cross-Lingual Representational Similarity Analysis of Acoustic Word Embeddings
Badr M. Abdullah, Iuliia Zaitova, Tania Avgustinova +2
How do neural networks "perceive" speech sounds from unknown languages? Does the typological similarity between the model's training language (L1) and an unknown language (L2) have…
Preventing Author Profiling through Zero-Shot Multilingual Back-Translation
David Ifeoluwa Adelani, Miaoran Zhang, Xiaoyu Shen +3
Documents as short as a single sentence may inadvertently reveal sensitive information about their authors, including e.g. their gender or ethnicity. Style transfer is an effective…
Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages
Dana Ruiter, Dietrich Klakow, Josef van Genabith +1
For most language combinations, parallel data is either scarce or simply unavailable. To address this, unsupervised machine translation (UMT) exploits large amounts of monolingual…
Exploring the Potential of Lexical Paraphrases for Mitigating Noise-Induced Comprehension Errors
Anupama Chingacham, Vera Demberg, Dietrich Klakow
Listening in noisy environments can be difficult even for individuals with a normal hearing thresholds. The speech signal can be masked by noise, which may lead to word mispercepti…
Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)
Michael A. Hedderich, Benjamin Roth, Katharina Kann +3
Welcome to WeaSuL 2021, the First Workshop on Weakly Supervised Learning, co-located with ICLR 2021. In this workshop, we want to advance theory, methods and tools for allowing exp…
Modeling Profanity and Hate Speech in Social Media with Semantic Subspaces
Vanessa Hahn, Dana Ruiter, Thomas Kleinbauer +1
Hate speech and profanity detection suffer from data sparsity, especially for languages other than English, due to the subjective nature of the tasks and the resulting annotation i…
Do Acoustic Word Embeddings Capture Phonological Similarity? An Empirical Study
Badr M. Abdullah, Marius Mosbach, Iuliia Zaitova +2
Several variants of deep neural networks have been successfully employed for building parametric models that project variable-duration spoken word segments onto fixed-size vector r…
To Share or not to Share: Predicting Sets of Sources for Model Transfer Learning
Lukas Lange, Jannik Strötgen, Heike Adel +1
In low-resource settings, model transfer can help to overcome a lack of labeled data for many tasks and domains. However, predicting useful transfer sources is a challenging proble…
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…
Emoji-Based Transfer Learning for Sentiment Tasks
Susann Boy, Dana Ruiter, Dietrich Klakow
Sentiment tasks such as hate speech detection and sentiment analysis, especially when performed on languages other than English, are often low-resource. In this study, we exploit t…
ANEA: Distant Supervision for Low-Resource Named Entity Recognition
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. However, to be used effectively, the distant…