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Found 15 papers, 4 papers with code
Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset
Our experiments show that the resulting dataset is challenging even for state-of-the-art hate speech detection models, and that training on GAHD clearly improves model robustness.
Can Chat GPT solve a Linguistics Exam?
It proved surprisingly successful in the task of broad phonetic transcription, but performed less well in the analysis of morphemes and phrases.
Turkish Native Language Identification
In this paper, we present the first application of Native Language Identification (NLI) for the Turkish language.
Evaluating the Effectiveness of Natural Language Inference for Hate Speech Detection in Languages with Limited Labeled Data
In this paper, we test whether natural language inference (NLI) models which perform well in zero- and few-shot settings can benefit hate speech detection performance in scenarios where only a limited amount of labeled data is available in the target language.
Exploring Hybrid Linguistic Features for Turkish Text Readability
This paper presents the first comprehensive study on automatic readability assessment of Turkish texts.
Scaling Native Language Identification with Transformer Adapters
Native language identification (NLI) is the task of automatically identifying the native language (L1) of an individual based on their language production in a learned language.
Hypothesis Engineering for Zero-Shot Hate Speech Detection
We find that the zero-shot baseline used for the initial error analysis already outperforms commercial systems and fine-tuned BERT-based hate speech detection models on HateCheck.
Using Multilingual Resources to Evaluate CEFRLex for Learner Applications
The Common European Framework of Reference for Languages (CEFR) defines six levels of learner proficiency, and links them to particular communicative abilities.
Dependency parsing for interaction detection in pharmacogenomics
We give an overview of our approach to the extraction of interactions between pharmacogenomic entities like drugs, genes and diseases and suggest classes of interaction types driven by data from PharmGKB and partly following the top level ontology WordNet and biomedical types from BioNLP.
