---
title: "SAAKTH at BEA 2026 Shared Task 1: L1-Aware English"
description: "The paper describes team SAAKTH's submission to the closed track of the BEA 2026 shared task… 33 references, cited by 1 paper here."
authors: ["Karthik Mattu", "Adit Dhall", "Arshad Naguru", "Shubh Sehgal", "Thejas Gowda", "Hakyung Sung"]
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-1.69/"
doi: "10.18653/v1/2026.bea-1.69"
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/mattu-2026-saakth-bea-2026-shared-task/"
---
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# SAAKTH at BEA 2026 Shared Task 1: L1-Aware English Vocabulary Difficulty Prediction with Hybrid Transformer and Psycholinguistic Features

**Authors:** Karthik Mattu, Adit Dhall, Arshad Naguru, Shubh Sehgal, Thejas Gowda and [Hakyung Sung](https://md.research.elamur.ai/free/en/authors/hakyung-sung/) · **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-1.69/) · [PDF](https://aclanthology.org/2026.bea-1.69.pdf) · [DOI 10.18653/v1/2026.bea-1.69](https://doi.org/10.18653/v1/2026.bea-1.69) · [OpenAlex](https://openalex.org/W7166837098)

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

## Summary

The paper describes team SAAKTH's submission to the closed track of the BEA 2026 shared task, which focuses on predicting vocabulary difficulty. The task is to estimate how hard an English word is for learners whose first language is Spanish, German, or Mandarin. Difficulty partly depends on the learner's L1: cross-L1 score correlations in the training data reach only r = 0.63–0.68. For that reason, the authors train a separate model for each L1 group.

Each model concatenates the first-token embedding from a fine-tuned XLM-RoBERTa-large with a small vector of handcrafted features. These include frequency, age of acquisition, concreteness, CEFR level, WordNet and orthographic cues, cognate similarity for the European languages, and character stroke count for Mandarin. A shallow MLP processes the combined vector. Predictions from five seeds are averaged and then linearly blended with a feature-only XGBoost model.

On the development set, this setup cuts RMSE by roughly 20–26% relative to the organizers' XLM-RoBERTa-base baseline. The results suggest that targeted L1-specific features help when data is limited. They also show weak transfer between typologically distant L1s.

## Key points

- **Data:** each L1 (es, de, cn) has 6,091 training and 677 dev items, derived from the British Council's KVL. Targets are GLMM-based difficulty scores.
- **Dev RMSE:** 0.997 (es), 1.002 (de), and 0.932 (cn), compared with baseline scores of 1.357, 1.328, and 1.175. Test RMSE was 1.045, 0.994, and 0.900.
- **Feature sets:** 19 (es), 20 (de), and 16 (cn) features, chosen from 21 candidates. Training used dual learning rates (1e-5 for the encoder, 1e-4 for the MLP head). The XGBoost blend weight α was 0.8 for es/de and 0.9 for cn.
- **Feature importance:** SHAP analysis ranks age of acquisition and SUBTLEX-US frequency highest for es/de. For cn, stroke count (Unihan) dominates with mean |SHAP| = 0.078. In ablations, removing cognate features unexpectedly helped German, and removing context features helped Spanish.
- **Cross-L1 transfer:** Spanish and German models transfer to each other with modest losses (+0.02–0.14 RMSE), while Mandarin-trained models lose about +0.52. Code is released on GitHub (aditdhall/bea2026-vocab-difficulty).

*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

- [Rochester Institute of Technology](https://md.research.elamur.ai/free/en/universities/rochester-institute-of-technology/) — University, Rochester, United States · 15 works · 5,233 citations

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## Cite

Cite as: Karthik Mattu, Adit Dhall, Arshad Naguru, Shubh Sehgal, Thejas Gowda and Hakyung Sung. 2026. SAAKTH at BEA 2026 Shared Task 1: L1-Aware English Vocabulary Difficulty Prediction with Hybrid Transformer and Psycholinguistic Features. 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{mattu2026saakth,
  title = {{SAAKTH at BEA 2026 Shared Task 1: L1-Aware English Vocabulary Difficulty Prediction with Hybrid Transformer and Psycholinguistic Features}},
  author = {Karthik Mattu, Adit Dhall, Arshad Naguru, Shubh Sehgal, Thejas Gowda and Hakyung Sung},
  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-1.69},
  url = {https://doi.org/10.18653/v1/2026.bea-1.69}
}
```

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*Updated 1 November 2026*