Quantifying mutual intelligibility gradients in Turkic languages using language models

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Science & Research — Mathematics & Computational Sciences, Social Sciences & Behavioral Studies · Depth: Expert, quick

Summary

This study introduces a neural language modeling method to quantify mutual intelligibility (MI) gradients within the Turkic language family. Researchers trained character-level LSTM models on IPA-transcribed naturalistic text from a source language, then fine-tuned them on six target Turkic languages with varying genealogical distances. Cross-lingual transfer was assessed using character-level cross-entropy (CE) loss, Area Under the Curve (AUC), and Rate of Change (ROC) to measure model generalization and adaptation. The findings indicate that these character-level models effectively approximate MI gradients, with closely related language pairs exhibiting lower CE loss and smaller AUC. Lexical similarity, local phonotactic overlap, and genealogical distance emerged as the most significant predictors of model convergence. This approach suggests a scalable method for modeling MI patterns and directional asymmetries among related languages.

Key takeaway

For computational linguists or researchers studying language evolution, this work offers a robust method for quantifying mutual intelligibility. You should consider adopting character-level LSTM models with IPA-transcribed data to analyze language relatedness, especially for closely related language families. This approach provides a scalable way to identify MI gradients and directional asymmetries, informing studies on language contact and divergence.

Key insights

Character-level LSTMs can quantify mutual intelligibility gradients and directional asymmetries across related languages.

Principles

• Mutual intelligibility is a gradient phenomenon.
• Cross-lingual transfer metrics reveal MI patterns.
• Lexical similarity predicts model convergence.

Method

Train character-level LSTM models on a source language using IPA-transcribed text, then fine-tune on target languages. Evaluate transfer with cross-entropy loss, AUC, and ROC.

In practice

• Apply LSTMs for language relatedness quantification.
• Use CE, AUC, ROC for cross-lingual transfer evaluation.

Topics

• Mutual Intelligibility
• Turkic Languages
• Character-level LSTMs
• Cross-lingual Transfer
• Computational Linguistics
• Language Evolution

Best for: NLP Engineer, AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.