Paper Citation Record · LEDGER

Comparative analysis of subword tokenization approaches for Indian languages

As of 8 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2505.16868.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.16868 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:56:45.919746Z

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

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External citation measurements

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Outbound references

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This paper cites an unresolved cited work.

Comparative analysis of subword tokenization approaches for Indian languages Unresolved cited work

Reference 1

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This paper cites An awkward disparity between BLEU/RIBES scores and human judgements in machine translation.

Comparative analysis of subword tokenization approaches for Indian languages An awkward disparity between BLEU/RIBES scores and human judgements in machine translation

Reference 2

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This paper cites METEOR: An automatic metric for MT evaluation with improved correlation with human judgments.

Comparative analysis of subword tokenization approaches for Indian languages METEOR: An automatic metric for MT evaluation with improved correlation with human judgments

Reference 3

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This paper cites A study of translation edit rate with targeted human annotation.

Comparative analysis of subword tokenization approaches for Indian languages A study of translation edit rate with targeted human annotation

Reference 4

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This paper cites chrF: character n -gram F-score for automatic MT evaluation.

Comparative analysis of subword tokenization approaches for Indian languages chrF: character n -gram F-score for automatic MT evaluation

Reference 5

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This paper cites COMET: A Neural Framework for MT Evaluation.

Comparative analysis of subword tokenization approaches for Indian languages COMET: A Neural Framework for MT Evaluation

Reference 6

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Comparative analysis of subword tokenization approaches for Indian languages Unresolved cited work

Reference 7

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This paper cites No Language Left Behind: Scaling Human-Centered Machine Translation.

Comparative analysis of subword tokenization approaches for Indian languages No Language Left Behind: Scaling Human-Centered Machine Translation

Reference 8

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This paper cites Neural Machine Translation of Rare Words with Subword Units.

Comparative analysis of subword tokenization approaches for Indian languages Neural Machine Translation of Rare Words with Subword Units

Reference 9

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Comparative analysis of subword tokenization approaches for Indian languages K., & Patra, B

Reference 10

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This paper cites B., Panda, D., Mishra, T.

Comparative analysis of subword tokenization approaches for Indian languages B., Panda, D., Mishra, T

Reference 11

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This paper cites B., Biradar, A., Mishra, T.

Comparative analysis of subword tokenization approaches for Indian languages B., Biradar, A., Mishra, T

Reference 12

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This paper cites Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.

Comparative analysis of subword tokenization approaches for Indian languages Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Reference 13

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Comparative analysis of subword tokenization approaches for Indian languages Unresolved cited work

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Comparative analysis of subword tokenization approaches for Indian languages Unresolved cited work

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This paper cites Statistical machine translation.

Comparative analysis of subword tokenization approaches for Indian languages Statistical machine translation

Reference 16

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This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

Comparative analysis of subword tokenization approaches for Indian languages Neural Machine Translation by Jointly Learning to Align and Translate

Reference 17

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Comparative analysis of subword tokenization approaches for Indian languages Unresolved cited work

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This paper cites Massively Multilingual Neural Machine Translation.

Comparative analysis of subword tokenization approaches for Indian languages Massively Multilingual Neural Machine Translation

Reference 19

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Comparative analysis of subword tokenization approaches for Indian languages Unresolved cited work

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This paper cites SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing.

Comparative analysis of subword tokenization approaches for Indian languages SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing

Reference 21

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Comparative analysis of subword tokenization approaches for Indian languages Machine Translation Approaches and Survey for Indian Languages

Reference 22

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Comparative analysis of subword tokenization approaches for Indian languages Unresolved cited work

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This paper cites Morphology: Indian languages and European languages.

Comparative analysis of subword tokenization approaches for Indian languages Morphology: Indian languages and European languages

Reference 24

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Comparative analysis of subword tokenization approaches for Indian languages Fast WordPiece Tokenization

Reference 25

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Comparative analysis of subword tokenization approaches for Indian languages NLTK documentation

Reference 26

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Comparative analysis of subword tokenization approaches for Indian languages D., Tetreault, J., & Stent, A

Reference 27

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This paper cites Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP.

Comparative analysis of subword tokenization approaches for Indian languages Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP

Reference 28

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Comparative analysis of subword tokenization approaches for Indian languages Character-based Neural Machine Translation

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This paper cites An Empirical Study of Tokenization Strategies for Various Korean NLP Tasks.

Comparative analysis of subword tokenization approaches for Indian languages An Empirical Study of Tokenization Strategies for Various Korean NLP Tasks

Reference 30

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This paper cites BPE-Dropout: Simple and Effective Subword Regularization.

Comparative analysis of subword tokenization approaches for Indian languages BPE-Dropout: Simple and Effective Subword Regularization

Reference 31

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Comparative analysis of subword tokenization approaches for Indian languages Byte Pair Encoding is Suboptimal for Language Model Pretraining

Reference 32

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Comparative analysis of subword tokenization approaches for Indian languages Using Integrated Gradients and Constituency Parse Trees to explain Linguistic Acceptability learnt by BERT

Reference 33

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Comparative analysis of subword tokenization approaches for Indian languages HAN: hierarchical association network for computing semantic relatedness

Reference 34

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This paper cites Meaningless yet meaningful: Morphology grounded subword- level NMT.

Comparative analysis of subword tokenization approaches for Indian languages Meaningless yet meaningful: Morphology grounded subword- level NMT

Reference 35

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Comparative analysis of subword tokenization approaches for Indian languages Unresolved cited work

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Comparative analysis of subword tokenization approaches for Indian languages BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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Comparative analysis of subword tokenization approaches for Indian languages AI as the next GPT: a Political -Economy Perspective

Reference 38

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Comparative analysis of subword tokenization approaches for Indian languages Unresolved cited work

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Comparative analysis of subword tokenization approaches for Indian languages fairseq: A Fast, Extensible Toolkit for Sequence Modeling

Reference 40

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Comparative analysis of subword tokenization approaches for Indian languages Unresolved cited work

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Comparative analysis of subword tokenization approaches for Indian languages Unresolved cited work

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Comparative analysis of subword tokenization approaches for Indian languages Moses-Statistical Machine Translation System

Reference 43

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Comparative analysis of subword tokenization approaches for Indian languages Adam: A Method for Stochastic Optimization

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Comparative analysis of subword tokenization approaches for Indian languages Post, A call for clarity in reporting BLEU scores, in Proceedings of the Third Conference on Machine Translation: Research Papers

Reference 45

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Comparative analysis of subword tokenization approaches for Indian languages Unresolved cited work

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Comparative analysis of subword tokenization approaches for Indian languages B., Choudhury, S., Mishra, T

Reference 47

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Pith citing papers

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