11 citations · 11 across the 4 of their papers we have counts for
11 papers
Boosting Transformers for Job Expression Extraction and Classification in a Low-Resource Setting
Lukas Lange, Heike Adel, Jannik Strötgen
In this paper, we explore possible improvements of transformer models in a low-resource setting. In particular, we present our approaches to tackle the first two of three subtasks…
Enriched Attention for Robust Relation Extraction
Heike Adel, Jannik Strötgen
The performance of relation extraction models has increased considerably with the rise of neural networks. However, a key issue of neural relation extraction is robustness: the mod…
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…
NLNDE at CANTEMIST: Neural Sequence Labeling and Parsing Approaches for Clinical Concept Extraction
Lukas Lange, Xiang Dai, Heike Adel +1
The recognition and normalization of clinical information, such as tumor morphology mentions, is an important, but complex process consisting of multiple subtasks. In this paper, w…
A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios
Michael A. Hedderich, Lukas Lange, Heike Adel +2
Deep neural networks and huge language models are becoming omnipresent in natural language applications. As they are known for requiring large amounts of training data, there is a…
FAME: Feature-Based Adversarial Meta-Embeddings for Robust Input Representations
Lukas Lange, Heike Adel, Jannik Strötgen +1
Combining several embeddings typically improves performance in downstream tasks as different embeddings encode different information. It has been shown that even models using embed…