---
title: "BoostedCats at BEA 2026 Shared Task 1: What Makes a Word"
description: "KVL scores are crowd-sourced test results from Spanish, German and Chinese speakers learning… 59 references, cited by 1 paper here."
authors: ["Jonas Mayer Martins", "Zhuojing Huang", "Aaricia Herygers", "Lisa Beinborn"]
year: 2026
venue: "Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026)"
source: "https://aclanthology.org/2026.bea-1.74/"
doi: "10.18653/v1/2026.bea-1.74"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/martins-2026-boostedcats-bea-2026-shared-task/"
---
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# BoostedCats at BEA 2026 Shared Task 1: What Makes a Word Hard to Learn? Modeling L1 Influence on English Vocabulary Difficulty

**Authors:** [Jonas Mayer Martins](https://md.research.elamur.ai/free/en/authors/jonas-mayer-martins/), [Zhuojing Huang](https://md.research.elamur.ai/free/en/authors/zhuojing-huang/), [Aaricia Herygers](https://md.research.elamur.ai/free/en/authors/aaricia-herygers/) and [Lisa Beinborn](https://md.research.elamur.ai/free/en/authors/lisa-beinborn/) · **Year:** 2026 · **Venue:** [Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026)](https://md.research.elamur.ai/free/en/venues/bea/) ([BEA 2026](https://md.research.elamur.ai/free/en/venues/bea/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.bea-1.74/) · [PDF](https://aclanthology.org/2026.bea-1.74.pdf) · [DOI 10.18653/v1/2026.bea-1.74](https://doi.org/10.18653/v1/2026.bea-1.74) · [OpenAlex](https://openalex.org/W7166804018)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/martins-2026-boostedcats-bea-2026-shared-task/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks why some English words are harder to learn than others and how this depends on the learner's first language. The authors took part in the closed track of the BEA 2026 Shared Task 1, which uses the Knowledge-based Vocabulary Lists (KVL). KVL scores are crowd-sourced test results from Spanish, German and Chinese speakers learning English, and a Rasch model turns them into per-word difficulty estimates.

For each L1, the authors trained a separate CatBoost model on 24 features in four groups:
- **Familiarity:** frequency, age of acquisition and CEFR level.
- **Meaning:** semantic complexity and concreteness.
- **Surface form:** orthographic complexity.
- **Transfer:** string similarity to the L1 translation.

Shapley values showed how much each group contributes to the predictions. Familiarity mattered most for all three L1s. Spanish and German predictions also depended heavily on orthographic transfer, which is a cognate effect. Chinese predictions relied only on familiarity and surface features. The Spanish and German models also worked well on each other's data. These interpretable, L1-specific estimates can help design vocabulary curricula and may carry over to related languages that lack training data.

## Key points
- **Data:** KVL draws on responses from more than 100,000 learners. Each L1 has 6,091 training, 677 development and 748 test items. The test format asks learners to recall the English spelling, given the first letter and a context sentence in their L1.
- **Model:** CatBoost regressors use 24 features. Familiarity has 11 features, taken from SUBTLEX-UK, Kuperman age-of-acquisition norms, CEFR-J and EFLLex. Meaning has 5 features, and the rest cover surface form and transfer.
- **Example of L1 effects:** "cable" scored 2.3 for Spanish speakers (identical spelling), 0.9 for German speakers (cognate *Kabel*) and 0.0 for Chinese speakers (电缆). Higher scores mean easier words.
- **Feature importance:** meaning features added little for every L1, probably because the task tests spelling rather than understanding.
- **Limitations:** transfer is measured only through character overlap, so phonology and calques are not captured. An interactive demo extends predictions to roughly 4,000–7,000 extra SUBTLEX-UK words per language.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Göttingen](https://md.research.elamur.ai/free/en/universities/university-of-gottingen/) — University, Göttingen, Germany · 17 works · 158 citations

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## Cite

Cite as: Jonas Mayer Martins, Zhuojing Huang, Aaricia Herygers and Lisa Beinborn. 2026. BoostedCats at BEA 2026 Shared Task 1: What Makes a Word Hard to Learn? Modeling L1 Influence on English Vocabulary Difficulty. Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{martins2026boostedcats,
  title = {{BoostedCats at BEA 2026 Shared Task 1: What Makes a Word Hard to Learn? Modeling L1 Influence on English Vocabulary Difficulty}},
  author = {Jonas Mayer Martins, Zhuojing Huang, Aaricia Herygers and Lisa Beinborn},
  booktitle = {Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.bea-1.74},
  url = {https://doi.org/10.18653/v1/2026.bea-1.74}
}
```

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*Updated 1 November 2026*