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ToneCL: Contrastive Learning for Few-Shot Syllable-Level Tone Classification

Paper recorded by Signals 4 on 2026-09-21 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-21 on arXiv · recorded by Signals 4 on 2026-09-22

Category: cs.CL · 自然语言处理 · first seen 2026-09-22

Abstract

Tone languages constitute over 50-70% of the world's languages, but the vast majority are low-resource, lacking the large transcribed corpora needed for automatic tone classification. Existing datasets are typically collected at the sentence level, whereas field linguists require fine-grained syllable-level annotations. We propose ToneCL, a lightweight contrastive learning framework for few-shot s

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#3 most recent of 212 cs.CL papers we have recorded · ↑ newer: SocioVerse2: A Longitudinal Dynamic Social Simulation Framework under · ↓ older: Human-LLM Deliberation as Interactive Proof: Conditions for Verifiabil
Cite this page: ToneCL: Contrastive Learning for Few-Shot Syllable-Level Tone Classification: the #3 most recent of 212 cs.CL papers we have recorded (as of 2026-09-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/tonecl-contrastive-learning-for-few-shot-syllable-level-tone-classification.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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