Comparative analysis of subword tokenization approaches for Indian languages

• URL: http://arxiv.org/abs/2505.16868v1
• Date: Thu, 22 May 2025 16:24:37 GMT
• Title: Comparative analysis of subword tokenization approaches for Indian languages
• Authors: Sudhansu Bala Das, Samujjal Choudhury, Tapas Kumar Mishra, Bidyut Kr. Patra,
• Abstract summary: Tokenization is the act of breaking down text into smaller parts, or tokens, that are easier for machines to process.<n>Subword tokenization enhances this process by breaking down words into smaller subword units.<n>It is useful in capturing the intricate structure of words in Indian languages (ILs), such as prefixes, suffixes, and other morphological variations.<n>This paper examines how different subword tokenization techniques, such as SentencePiece, Byte Pair, and WordPiece Tokenization, affect ILs.
• Score: 5.012314384895538
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tokenization is the act of breaking down text into smaller parts, or tokens, that are easier for machines to process. This is a key phase in machine translation (MT) models. Subword tokenization enhances this process by breaking down words into smaller subword units, which is especially beneficial in languages with complicated morphology or a vast vocabulary. It is useful in capturing the intricate structure of words in Indian languages (ILs), such as prefixes, suffixes, and other morphological variations. These languages frequently use agglutinative structures, in which words are formed by the combination of multiple morphemes such as suffixes, prefixes, and stems. As a result, a suitable tokenization strategy must be chosen to address these scenarios. This paper examines how different subword tokenization techniques, such as SentencePiece, Byte Pair Encoding (BPE), and WordPiece Tokenization, affect ILs. The effectiveness of these subword tokenization techniques is investigated in statistical, neural, and multilingual neural machine translation models. All models are examined using standard evaluation metrics, such as the Bilingual Evaluation Understudy (BLEU) score, TER, METEOR, CHRF, RIBES, and COMET. Based on the results, it appears that for the majority of language pairs for the Statistical and Neural MT models, the SentencePiece tokenizer continuously performed better than other tokenizers in terms of BLEU score. However, BPE tokenization outperformed other tokenization techniques in the context of Multilingual Neural Machine Translation model. The results show that, despite using the same tokenizer and dataset for each model, translations from ILs to English surpassed translations from English to ILs.

Related papers

• MorphTok: Morphologically Grounded Tokenization for Indian Languages [23.58043476541051]
  Tokenization is a crucial step in NLP, especially with the rise of large language models (LLMs)<n>We propose morphology-aware segmentation as a pre-tokenization step prior to applying subword tokenization.<n>We also introduce Constrained BPE, an extension to the traditional BPE algorithm that incorporates script-specific constraints.
  arXiv  Detail & Related papers  (2025-04-14T15:44:45Z)
• Beyond Literal Token Overlap: Token Alignability for Multilinguality [53.680462160878925]
  We propose subword token alignability as a new way to understand the impact and quality of multilingual tokenisation.<n>In particular, this metric predicts multilinguality much better when scripts are disparate and the overlap of literal tokens is low.<n>We recommend our subword token alignability metric for identifying optimal language pairs for cross-lingual transfer.
  arXiv  Detail & Related papers  (2025-02-10T13:50:12Z)
• An Analysis of BPE Vocabulary Trimming in Neural Machine Translation [56.383793805299234]
  vocabulary trimming is a postprocessing step that replaces rare subwords with their component subwords. We show that vocabulary trimming fails to improve performance and is even prone to incurring heavy degradation.
  arXiv  Detail & Related papers  (2024-03-30T15:29:49Z)
• Rethinking Tokenization: Crafting Better Tokenizers for Large Language Models [0.0]
  Tokenization significantly influences language models(LMs)' performance. This paper traces the evolution of tokenizers from word-level to subword-level, analyzing how they balance tokens and types. The Less-is-Better (LiB) model could be a new approach for LLM tokenizer.
  arXiv  Detail & Related papers  (2024-03-01T10:03:07Z)
• How Robust is Neural Machine Translation to Language Imbalance in Multilingual Tokenizer Training? [86.48323488619629]
  We analyze how translation performance changes as the data ratios among languages vary in the tokenizer training corpus. We find that while relatively better performance is often observed when languages are more equally sampled, the downstream performance is more robust to language imbalance than we usually expected.
  arXiv  Detail & Related papers  (2022-04-29T17:50:36Z)
• A Vocabulary-Free Multilingual Neural Tokenizer for End-to-End Task Learning [8.052271364177988]
  Subword tokenization is a commonly used input pre-processing step in most recent NLP models. We propose a vocabulary-free neural tokenizer by distilling segmentation information from subword tokenization. Our tokenizer consistently improves performance on multilingual (NLI) and code-switching (sentiment analysis) tasks.
  arXiv  Detail & Related papers  (2022-04-22T16:50:49Z)
• Impact of Tokenization on Language Models: An Analysis for Turkish [2.4660652494309936]
  We train tokenizers and pretrain medium-sized language models using RoBERTa pretraining procedure on the Turkish split of the OSCAR corpus. Our experiments, supported by statistical tests, reveal that Morphological-level tokenizer has challenging performance with de facto tokenizers. We find that increasing the vocabulary size improves the performance of Morphological and Word-level tokenizers more than that of de facto tokenizers.
  arXiv  Detail & Related papers  (2022-04-19T12:01:46Z)
• More Than Words: Collocation Tokenization for Latent Dirichlet Allocation Models [71.42030830910227]
  We propose a new metric for measuring the clustering quality in settings where the models differ. We show that topics trained with merged tokens result in topic keys that are clearer, more coherent, and more effective at distinguishing topics than those unmerged models.
  arXiv  Detail & Related papers  (2021-08-24T14:08:19Z)
• Charformer: Fast Character Transformers via Gradient-based Subword Tokenization [50.16128796194463]
  We propose a new model inductive bias that learns a subword tokenization end-to-end as part of the model. We introduce a soft gradient-based subword tokenization module (GBST) that automatically learns latent subword representations from characters. We additionally introduce Charformer, a deep Transformer model that integrates GBST and operates on the byte level.
  arXiv  Detail & Related papers  (2021-06-23T22:24:14Z)
• Byte Pair Encoding is Suboptimal for Language Model Pretraining [49.30780227162387]
  We analyze differences between unigram LM tokenization and byte-pair encoding (BPE) We find that the unigram LM tokenization method matches or outperforms BPE across downstream tasks and two languages. We hope that developers of future pretrained LMs will consider adopting the unigram LM method over the more prevalent BPE.
  arXiv  Detail & Related papers  (2020-04-07T21:21:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.