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New system CANDLE uses CTC for Arabic text noise deduplication

By PulseAugur Editorial · [2 sources] ·

Researchers have developed CANDLE, a novel system for character-level Arabic noise deduplication. This system utilizes Connectionist Temporal Classification (CTC) to frame normalization as a sequence alignment problem, a method not previously applied to character deduplication. Evaluated on various benchmarks, CANDLE achieved a Sentence Error Rate as low as 5.37% and significantly outperformed a classification-based baseline. The system was further distilled into a smaller, 2-layer model with minimal performance loss, offering practical benefits such as a reduction in tokenizer fertility for Arabic LLMs, thereby lowering inference costs and improving context window utilization. AI

IMPACT This research could lead to more efficient and cost-effective processing of Arabic text in LLMs.

RANK_REASON The cluster contains an academic paper detailing a new method and system for text processing.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New system CANDLE uses CTC for Arabic text noise deduplication

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Faris Alasmary, Taif Nono, Orjuwan Zaafarani, Kholood Al Tabash, Ahmad Ghannam, Anas Salamah, Shouq Sadah, Lahouari Ghouti ·

    CANDLE: Character-level Arabic Noise Deduplication using Lightweight Encoder

    arXiv:2606.24758v1 Announce Type: new Abstract: Handling repeated characters in text can be tricky, since they can represent either the correct spelling of a word or informal character elongation often seen in social media posts. We present CANDLE, a lightweight system for charac…

  2. arXiv cs.CL TIER_1 English(EN) · Lahouari Ghouti ·

    CANDLE: Character-level Arabic Noise Deduplication using Lightweight Encoder

    Handling repeated characters in text can be tricky, since they can represent either the correct spelling of a word or informal character elongation often seen in social media posts. We present CANDLE, a lightweight system for character-level Arabic noise deduplication that addres…

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