Papers by Benjamin Minixhofer
Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation (2024.emnlp-main)
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| Challenge: | Sentence segmentation is a key task in many NLP systems, but no prior method has achieved all of the features needed to segment a text into sentences. |
| Approach: | They propose a new model that uses punctuation to enhance robustness and adaptability. |
| Outcome: | The proposed model outperforms baselines across 8 corpora across diverse domains and languages and is available under the MIT license. |
Navigating the Alignment-Calibration Trade-off: A Pareto-Superior Frontier via Model Merging (2026.findings-acl)
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| Challenge: | We show that the "alignment tax" of post-training is framed as a drop in task accuracy. |
| Approach: | They propose a more holistic view of the alignment tax by framing it as a drop in accuracy and a degradation of model calibration. |
| Outcome: | The proposed method improves accuracy beyond both parents while recovering calibration lost during alignment. |
Where’s the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation (2023.acl-long)
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| Challenge: | Prior sentence segmentation tools rely on punctuation or require a large amount of training data . a new method for multilingual sentence segmenting is proposed to replace the best prior tools by using only sentence-segmented examples. |
| Approach: | They propose a punctuation-agnostic sentence segmentation method that uses newline characters which implicitly perform segmentation into paragraphs. |
| Outcome: | The proposed method outperforms all prior best sentence segmentation tools by 6.1% F1 points. |
HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crises Response (2022.findings-emnlp)
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Selim Fekih, Nicolo’ Tamagnone, Benjamin Minixhofer, Ranjan Shrestha, Ximena Contla, Ewan Oglethorpe, Navid Rekabsaz
| Challenge: | During humanitarian crises, a quick and accurate analysis of relevant data is critical to a timely and effective response. |
| Approach: | They introduce and release a multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response domain. |
| Outcome: | The proposed dataset provides documents in three languages and covers a variety of humanitarian crises from 2018 to 2021 across the globe. |
WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models (2022.naacl-main)
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| Challenge: | Existing methods to train large pretrained language models require more computational resources and are expensive to train in other languages. |
| Approach: | They propose a method to transfer pretrained language models to new languages using subword-based tokenization and embeddings. |
| Outcome: | The proposed method outperforms existing methods on low-resource languages and makes training large models more accessible and less damaging to the environment. |
Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-Tuning (2021.findings-acl)
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| Challenge: | Recent advances in transfer learning have brought significant improvements to many natural language processing tasks. |
| Approach: | They propose a method of fitting a GBDT head on the features computed during finetuning to increase performance without additional computation by the neural network. |
| Outcome: | The proposed method improves on several NLI datasets using a strong baseline model (RoBERTa-large) with MNLI pretraining. |
CompoundPiece: Evaluating and Improving Decompounding Performance of Language Models (2023.emnlp-main)
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| Challenge: | Currently, there is no dataset containing compound and non-compound words across languages . however, current LLMs perform poorly on words tokenized unfavorably by subword tokenization. |
| Approach: | They propose to use a Wiktionary dataset to evaluate large language models on decompounding . they find that current LLMs perform poorly on words tokenized unfavorably . |
| Outcome: | The proposed model outperforms the best unsupervised models by 13.9% accuracy on average. |
Retrofitting Large Language Models with Dynamic Tokenization (2025.acl-long)
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| Challenge: | Current language models use a static tokenizer, which results in degraded efficiency and language capabilities. |
| Approach: | They propose a way to dynamically decide on token boundaries based on input text . they merge frequent subword sequences in a batch and apply a hypernetwork to compute token embeddings . |
| Outcome: | The proposed method reduces token sequence lengths by >20% across 14 languages while promoting fairness across languages. |