Papers by Arij Riabi
Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark (2024.naacl-long)
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Stephen Mayhew, Terra Blevins, Shuheng Liu, Marek Suppa, Hila Gonen, Joseph Marvin Imperial, Börje Karlsson, Peiqin Lin, Nikola Ljubešić, Lester James Miranda, Barbara Plank, Arij Riabi, Yuval Pinter
| Challenge: | In named entity recognition, the majority of annotation efforts are centered on English, and cross-lingual transfer performance remains brittle. |
| Approach: | They propose to develop gold-standard named entity recognition benchmarks in many languages using a cross-lingual consistent schema. |
| Outcome: | The proposed benchmarks will be released to the public in 2022 . they will provide baselines on in-language and cross-lingual learning settings. |
Synthetic Data Augmentation for Zero-Shot Cross-Lingual Question Answering (2021.emnlp-main)
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| Challenge: | Existing methods to improve Question Answering performance on non-English data are expensive and limited to evaluation set. |
| Approach: | They propose a method to improve Question Answering performance without additional annotations by leveraging Question Generation models to produce synthetic samples in a cross-lingual fashion. |
| Outcome: | The proposed method outperforms baselines on four datasets in English significantly . the proposed model outperformed baselines in english and is comparable to the validation set of the original SQuAD. |
Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detection (2025.coling-main)
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| Challenge: | Existing datasets and models fail to address the complexities of multilingual data, authors say . detection of radical content on online platforms has become an increasingly pressing concern . |
| Approach: | They propose a publicly available multilingual dataset annotated with radicalization levels, calls for action, and named entities in English, French, and Arabic. |
| Outcome: | The proposed dataset is annotated with radicalization levels, calls for action, and named entities in English, French, and Arabic. |
Multilingual Auxiliary Tasks Training: Bridging the Gap between Languages for Zero-Shot Transfer of Hate Speech Detection Models (2022.findings-aacl)
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| Challenge: | Zero-shot cross-lingual transfer learning has been shown to be challenging for tasks involving a lot of linguistic specificities or when a cultural gap is present between languages, such as hate speech detection. |
| Approach: | They propose to train on multilingual auxiliary tasks to improve zero-shot transfer of hate speech detection models across languages by bringing a cross-lingual knowledge proxy to the task. |
| Outcome: | The proposed methods improve zero-shot transfer of hate speech detection models across languages and domains using multilingual auxiliary tasks fine-tuned. |
IYKYK: Using language models to decode extremist cryptolects (2026.eacl-long)
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Christine de Kock, Arij Riabi, Zeerak Talat, Michael Sejr Schlichtkrull, Pranava Madhyastha, Eduard Hovy
| Challenge: | Extremist groups develop complex in-group language to exclude or mislead outsiders . general purpose LLMs cannot consistently detect or decode extremist language . |
| Approach: | They evaluate the ability of current language technologies to detect and interpret the cryptolects of two online extremist platforms. |
| Outcome: | The proposed models can detect and interpret extremist language better than current models. |