Transformers Match Traditional Features in Multilingual Readability Assessment
Researchers compared transformer‑based language models with classic feature‑driven classifiers on the ReadMe++ multilingual readability dataset covering Arabic, English, French, Hindi and Russian. Using SHAP to expose the most influential linguistic cues for the traditional models, they built TCAV concept sets and probed both the multilingual XLM‑R encoder and language‑specific transformers. The…
Key points
- Transformers recover surface‑length, syntactic and lexical‑diversity cues across five languages.
- Language‑specific encoders align more closely with traditional models than XLM‑R.
- Linear probing shows separability but not directional influence on readability features.
Alignment strength varies across model families, languages and network layers: language‑specific encoders track the traditional feature patterns more closely than the cross‑lingual XLM‑R. However, high linear separability of representations does not guarantee a causal influence on readability predictions, highlighting limits of linear probing for count‑based linguistic features. The study underscores that transformer models can internalize classic readability cues while still operating as black‑box predictors.
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