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Found 9 papers, 5 papers with code
Empathy-driven Arabic Conversational Chatbot
However, the dataset is not large enough to develop very complex encoder-decoder models.
Stanceosaurus 2.0: Classifying Stance Towards Russian and Spanish Misinformation
The Stanceosaurus corpus (Zheng et al., 2022) was designed to provide high-quality, annotated, 5-way stance data extracted from Twitter, suitable for analyzing cross-cultural and cross-lingual misinformation.
Reducing Privacy Risks in Online Self-Disclosures with Language Models
Motivated by the user feedback, we introduce the task of self-disclosure abstraction, which is paraphrasing disclosures into less specific terms while preserving their utility, e. g., "Im 16F" to "I'm a teenage girl".
Revisiting non-English Text Simplification: A Unified Multilingual Benchmark
However, less work has been done on multilingual text simplification due to the lack of a diverse evaluation benchmark that covers complex-simple sentence pairs in many languages.
Ranked #1 on
Text Simplification
on WikiLargeFR
ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment
We present a systematic study and comprehensive evaluation of large language models for automatic multilingual readability assessment.
Having Beer after Prayer? Measuring Cultural Bias in Large Language Models
In this paper, we show that multilingual and Arabic monolingual LMs exhibit bias towards entities associated with Western culture.
Stanceosaurus: Classifying Stance Towards Multilingual Misinformation
We present Stanceosaurus, a new corpus of 28, 033 tweets in English, Hindi, and Arabic annotated with stance towards 251 misinformation claims.
Clustering Plotted Data by Image Segmentation
We describe the method and compare it to ten other clustering methods on synthetic data to illustrate its advantages and disadvantages.
Empathetic BERT2BERT Conversational Model: Learning Arabic Language Generation with Little Data
The shortcomings of NLG encoder-decoder models are primarily due to the lack of Arabic datasets suitable to train NLG models such as conversational agents.
Empathetic Response Generation
Natural Language Understanding
+3
