BHARATI: Morphology-Aware Tokenizers for Classical Indian Languages with Subword Fertility Analysis
Quick Answer
BHARATI introduces morphology-aware tokenizers for classical Indian languages, outperforming GPT-2 and multilingual SentencePiece.
Quick Take
The v3 tokenizer reduces sequence length by 90% compared to GPT-2 and 25% against mBART-50, enhancing context for downstream models.
Key Points
- BHARATI tokenizers are trained on a 781 MB corpus across seven languages.
- Subword fertility analysis shows v3 averages 2.6 tokens per IKS term.
- v3 significantly reduces sequence length, improving effective context for models.
- Open licenses for tokenizer models, training scripts, and benchmarks are provided.
- Three tokenizer versions were developed, expanding language support progressively.
Paper Resources
Source Excerpt
Standard subword tokenization algorithms such as Byte-Pair Encoding (BPE) and SentencePiece are trained predominantly on modern language corpora and produce inefficient segmentations when applied to classical Indian languages. Sanskrit, Tamil, and other classical Indic languages exhibit agglutinative morphology, productive sandhi (phonological fusion at word boundaries), and domain-specific vocabularies absent from general-purpose training data. This paper presents BHARATI, a set of SentencePiec
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