---
title: "Who cites Superbizarre Is Not Superb: Derivational…"
description: "The papers of the Elamur Research library that cite «Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words»…"
canonical: "https://research.elamur.ai/free/en/papers/lex-2021-hofmann-superbizarre-derivational/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Superbizarre Is Not Superb: Derivational…](https://md.research.elamur.ai/free/en/papers/lex-2021-hofmann-superbizarre-derivational/) / cited by

# Who cites «Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words»

61 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/lex-2021-hofmann-superbizarre-derivational/)

1. [Benchmarking Byte-Pair Encoding Tokenizers on Different Languages with Bits per Byte](https://aclanthology.org/2026.mellm-1.27/) *— 2026 · Proceedings of the 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026)*
2. [Do Diacritics Matter? Evaluating the Impact of Arabic Diacritics on Tokenization and LLM Benchmarks](https://md.research.elamur.ai/free/en/papers/aclbulk-2026-inoue-do-diacritics-matter-evaluating-impact/) *— our card · 2026 · Findings of the Association for Computational Linguistics: EACL 2026*
3. [Morpheme Matters: Morpheme-Based Subword Tokenization for Korean Language Models](https://aclanthology.org/2026.eacl-short.22/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)*
4. [On the Proper Treatment of Units in Surprisal Theory](https://aclanthology.org/2026.acl-long.1485/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
5. [ReBPE: Iteratively Improving the Internal Structure of a Structured Tokeniser by Mining its Internal Structure](https://aclanthology.org/2026.findings-eacl.211/) *— 2026 · Findings of the Association for Computational Linguistics: EACL 2026*
6. [Tokenization and Morphological Fidelity in Uralic NLP: A Cross-Lingual Evaluation](https://arxiv.org/abs/2602.04241) *— 2026 · arXiv:2602.04241 · Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)*
7. [Vocab Diet: Reshaping the Vocabulary of LLMs via Vector Arithmetic](https://aclanthology.org/2026.findings-acl.1618/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
8. [AdaptiVocab: Enhancing LLM Efficiency in Focused Domains through Lightweight Vocabulary Adaptation](https://arxiv.org/abs/2503.19693) *— 2025 · arXiv:2503.19693*
9. [Case–Number Dissociation in Finnish Noun Embeddings:fastText vs. BERT Layer Effects](https://aclanthology.org/2025.iwclul-1.16/) *— 2025 · Proceedings of the 10th International Workshop on Computational Linguistics for Uralic Languages*
10. [Evaluating Morphological Compositional Generalization in Large Language Models](https://aclanthology.org/2025.naacl-long.59/) *— 2025 · NAACL 2025 (Albuquerque)*
11. [From Smør-re-brød to Subwords: Training LLMs on Danish, One Morpheme at a Time](https://arxiv.org/abs/2504.01540) *— 2025 · arXiv:2504.01540*
12. [GRaMPa: Subword Regularisation by Skewing Uniform Segmentation Distributions with an Efficient Path-counting Markov Model](https://aclanthology.org/2025.acl-long.1180/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
13. [How Much Semantic Information is Available in Large Language Model Tokens?](https://aclanthology.org/2025.tacl-1.20/) *— 2025 · Transactions of the Association for Computational Linguistics*
14. [Incorporating Domain Knowledge into Materials Tokenization](https://aclanthology.org/2025.acl-long.474/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
15. [KinyaColBERT: A Lexically Grounded Retrieval Model for Low-Resource Retrieval-Augmented Generation](https://arxiv.org/abs/2507.03241) *— 2025 · arXiv:2507.03241*
16. [Rethinking Vocabulary Augmentation: Addressing the Challenges of Low-Resource Languages in Multilingual Models](https://aclanthology.org/2025.coling-main.197/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
17. [Tokenization Changes Meaning in Large Language Models: Evidence from Chinese](https://aclanthology.org/2025.cl-3.3/) *— 2025 · Computational Linguistics*
18. [Towards the Machine Translation of Scientific Neologisms](https://aclanthology.org/2025.coling-main.63/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
19. [Unlike “Likely”, “Unlike” is Unlikely: BPE-based Segmentation hurts Morphological Derivations in LLMs](https://aclanthology.org/2025.coling-main.348/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
20. [Why do language models perform worse for morphologically complex languages?](https://aclanthology.org/2025.coling-main.441/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
21. [Analyzing the Understanding of Morphologically Complex Words in Large Language Models](https://aclanthology.org/2024.lrec-main.90/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
22. [BPE Gets Picky: Efficient Vocabulary Refinement During Tokenizer Training](https://aclanthology.org/2024.emnlp-main.925/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
23. [BPE-knockout: Pruning Pre-existing BPE Tokenisers with Backwards-compatible Morphological Semi-supervision](https://aclanthology.org/2024.naacl-long.324/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
24. [Bits and Pieces: Investigating the Effects of Subwords in Multi-task Parsing across Languages and Domains](https://aclanthology.org/2024.lrec-main.215/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
25. [Derivational Morphology Reveals Analogical Generalization in Large Language Models](https://arxiv.org/abs/2411.07990) *— 2024 · arXiv:2411.07990*
26. [DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical Domain](https://aclanthology.org/2024.lrec-main.478/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
27. [Fitting a Square Peg into a Round Hole: Creating a UniMorph dataset of Kanien”kéha Verbs](https://aclanthology.org/2024.computel-1.7/) *— 2024 · Proceedings of the Seventh Workshop on the Use of Computational Methods in the Study of Endangered Languages*
28. [Greed is All You Need: An Evaluation of Tokenizer Inference Methods](https://aclanthology.org/2024.acl-short.73/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
29. [HeSum: a Novel Dataset for Abstractive Text Summarization in Hebrew](https://md.research.elamur.ai/free/en/papers/findings-2024-argaman-hesum-novel-dataset-abstractive-text/) *— our card · 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
30. [LLMSegm: Surface-level Morphological Segmentation Using Large Language Model](https://aclanthology.org/2024.lrec-main.933/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
31. [Latin Treebanks in Review: An Evaluation of Morphological Tagging Across Time](https://aclanthology.org/2024.ml4al-1.21/) *— 2024 · Proceedings of the 1st Workshop on Machine Learning for Ancient Languages (ML4AL 2024)*
32. [Morphological evaluation of subwords vocabulary used by BETO language model](https://arxiv.org/abs/2410.02283) *— 2024 · arXiv:2410.02283*
33. [Subword Segmentation in LLMs: Looking at Inflection and Consistency](https://aclanthology.org/2024.emnlp-main.672/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
34. [The Impact of Word Splitting on the Semantic Content of Contextualized Word Representations](https://aclanthology.org/2024.tacl-1.17/) *— 2024 · Transactions of the Association for Computational Linguistics*
35. [Time Machine GPT](https://aclanthology.org/2024.findings-naacl.208/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
36. [Tokenization Is More Than Compression](https://aclanthology.org/2024.emnlp-main.40/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
37. [Unpacking Tokenization: Evaluating Text Compression and its Correlation with Model Performance](https://aclanthology.org/2024.findings-acl.134/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
38. [Word Boundary Information Isn't Useful for Encoder Language Models](https://aclanthology.org/2024.repl4nlp-1.10/) *— 2024 · Proceedings of the 9th Workshop on Representation Learning for NLP (RepL4NLP-2024)*
39. [Analyzing Cognitive Plausibility of Subword Tokenization](https://aclanthology.org/2023.emnlp-main.272/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
40. [BERTwich: Extending BERT”s Capabilities to Model Dialectal and Noisy Text](https://aclanthology.org/2023.findings-emnlp.1037/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
41. [Biomedical Language Models are Robust to Sub-optimal Tokenization](https://aclanthology.org/2023.bionlp-1.32/) *— 2023 · Proceedings of the 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks*
42. [Can Pretrained Language Models Derive Correct Semantics from Corrupt Subwords under Noise?](https://aclanthology.org/2023.starsem-1.15/) *— 2023 · Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)*
43. [CompoundPiece: Evaluating and Improving Decompounding Performance of Language Models](https://aclanthology.org/2023.emnlp-main.24/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
44. [Counting the Bugs in ChatGPT”s Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model](https://md.research.elamur.ai/free/en/papers/lex-2023-weissweiler-chatgpt-wugs-morphology/) *— our card · 2023 · EMNLP 2023 (Singapore)*
45. [Effects of sub-word segmentation on performance of transformer language models](https://aclanthology.org/2023.emnlp-main.459/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
46. [Entropy-guided Vocabulary Augmentation of Multilingual Language Models for Low-resource Tasks](https://aclanthology.org/2023.findings-acl.548/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
47. [Explicit Morphological Knowledge Improves Pre-training of Language Models for Hebrew](https://md.research.elamur.ai/free/en/papers/langstream-2023-gueta-explicit-morphological-knowledge-improves-pre/) *— our card · 2023 · arXiv:2311.00658*
48. [Incorporating Context into Subword Vocabularies](https://aclanthology.org/2023.eacl-main.45/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
49. [Language Model Tokenizers Introduce Unfairness Between Languages](https://arxiv.org/abs/2305.15425) *— 2023 · arXiv:2305.15425*
50. [Paloma: A Benchmark for Evaluating Language Model Fit](https://arxiv.org/abs/2312.10523) *— 2023 · arXiv:2312.10523*
51. [Sentence Embedding Models for Ancient Greek Using Multilingual Knowledge Distillation](https://aclanthology.org/2023.alp-1.2/) *— 2023 · Proceedings of the Ancient Language Processing Workshop*
52. [BECEL: Benchmark for Consistency Evaluation of Language Models](https://aclanthology.org/2022.coling-1.324/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
53. [Constructing a Derivational Morphology Resource with Transformer Morpheme Segmentation](https://aclanthology.org/2022.konvens-1.12/) *— 2022 · Proceedings of the 18th Conference on Natural Language Processing (KONVENS 2022)*
54. [Improving Tokenisation by Alternative Treatment of Spaces](https://aclanthology.org/2022.emnlp-main.786/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
55. [Morphological Processing of Low-Resource Languages: Where We Are and What”s Next](https://aclanthology.org/2022.findings-acl.80/) *— 2022 · Findings of ACL 2022 (Dublin)*
56. [Morphology Without Borders: Clause-Level Morphology](https://aclanthology.org/2022.tacl-1.83/) *— 2022 · Transactions of the Association for Computational Linguistics*
57. [Signal in Noise: Exploring Meaning Encoded in Random Character Sequences with Character-Aware Language Models](https://aclanthology.org/2022.acl-long.492/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
58. [Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun Entailment](https://aclanthology.org/2022.coling-1.359/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
59. [Training on Lexical Resources](https://aclanthology.org/2022.lrec-1.676/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
60. [What do Large Language Models Learn beyond Language?](https://aclanthology.org/2022.findings-emnlp.516/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
61. [Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP](https://arxiv.org/abs/2112.10508) *— 2021 · arXiv:2112.10508*