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Found 13 papers, 9 papers with code
Constrained C-Test Generation via Mixed-Integer Programming
This work proposes a novel method to generate C-Tests; a deviated form of cloze tests (a gap filling exercise) where only the last part of a word is turned into a gap.
Surveying (Dis)Parities and Concerns of Compute Hungry NLP Research
Many recent improvements in NLP stem from the development and use of large pre-trained language models (PLMs) with billions of parameters.
Lessons Learned from a Citizen Science Project for Natural Language Processing
Citizen Science is an alternative to crowdsourcing that is relatively unexplored in the context of NLP.
Rediscovering Hashed Random Projections for Efficient Quantization of Contextualized Sentence Embeddings
Training and inference on edge devices often requires an efficient setup due to computational limitations.
Efficient Methods for Natural Language Processing: A Survey
Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows.
Transformers with Learnable Activation Functions
In contrast to conventional, predefined activation functions, RAFs can adaptively learn optimal activation functions during training according to input data.
TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation
We evaluate TexPrax in a user-study with German factory employees who ask their colleagues for solutions on problems that arise during their daily work.
Annotation Curricula to Implicitly Train Non-Expert Annotators
Annotation studies often require annotators to familiarize themselves with the task, its annotation scheme, and the data domain.
Investigating label suggestions for opinion mining in German Covid-19 social media
This work investigates the use of interactively updated label suggestions to improve upon the efficiency of gathering annotations on the task of opinion mining in German Covid-19 social media data.
Empowering Active Learning to Jointly Optimize System and User Demands
Existing approaches to active learning maximize the system performance by sampling unlabeled instances for annotation that yield the most efficient training.
Manipulating the Difficulty of C-Tests
We propose two novel manipulation strategies for increasing and decreasing the difficulty of C-tests automatically.
Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems
Visual modifications to text are often used to obfuscate offensive comments in social media (e. g., "! d10t") or as a writing style ("1337" in "leet speak"), among other scenarios.
