Who cites «Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging»

13 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. ← the card

1. AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs — 2025 · Proceedings of the 31st International Conference on Computational Linguistics
2. Fanar: An Arabic-Centric Multimodal Generative AI Platform — 2025 · arXiv:2501.13944
3. Benchmarking LLaMA-3 on Arabic Language Generation Tasks — 2024 · Proceedings of the Second Arabic Natural Language Processing Conference
4. Optimizing Word Alignments with Better Subword Tokenization — 2021 · Proceedings of Machine Translation Summit XVIII: Research Track
5. AraBench: Benchmarking Dialectal Arabic-English Machine Translation — 2020 · Proceedings of the 28th International Conference on Computational Linguistics
6. One Size Does Not Fit All: Comparing NMT Representations of Different Granularities — 2019 · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
7. How Robust Are Character-Based Word Embeddings in Tagging and MT Against Wrod Scramlbing or Randdm Nouse? — 2018 · Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)
8. Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation — 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
9. Morphological Word Embeddings for Arabic Neural Machine Translation in Low-Resource Settings — our card · 2018 · Proceedings of the Second Workshop on Subword/Character LEvel Models
10. Neural Machine Translation Training in a Multi-Domain Scenario — 2017 · Proceedings of the 14th International Conference on Spoken Language Translation
11. Synthetic and Natural Noise Both Break Neural Machine Translation — 2017 · arXiv:1711.02173
12. What do Neural Machine Translation Models Learn about Morphology? — 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
13. asgari-2026-morphbpe-morphology-aware-tokenization-efficient

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