The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing
Open accessAbstract
This paper describes SentencePiece, a language-independent subword tokenizer and detokenizer designed for Neural-based text processing, including Neural Machine Translation.It provides open-source C++ and Python implementations for subword units.While existing subword segmentation tools assume that the input is pre-tokenized into word sequences, SentencePiece can train subword models directly from raw sentences, which allows us to make a purely end-to-end and language independent system.We perform a validation experiment of NMT on English-Japanese machine translation, and find that it is possible to achieve comparable accuracy to direct subword training from raw sentences.We also compare the performance of subword training and segmentation with various configurations.SentencePiece is available under the Apache 2 license at https://github.com/google/sentencepiece.
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“Its seven billion parameters are pre-trained on two trillion sentencepiece (Kudo & Richardson, 2018) tokens, 90% of which is in English.”
From The Linear Representation Hypothesis and the Geometry of Large Language Models (2023) · cites this paper as Kudo & Richardson, 2018
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BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344
Effective Approaches to Attention-based Neural Machine Translation
Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1166 · Cited by 8,382
A Call for Clarity in Reporting BLEU Scores
Post, M. (2018) · Proceedings of the Third Conference on Machine Translation: Research Papers · doi:10.18653/v1/w18-6319 · Cited by 2,112
Vinyals, O., & Le, Q. (2015) · arXiv · doi:10.48550/arxiv.1506.05869 · Cited by 1,400
Cited by
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929
XLNet: Generalized Autoregressive Pretraining for Language Understanding
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1906.08237 · Cited by 6,687
Unsupervised Cross-lingual Representation Learning at Scale
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.747 · Cited by 5,637
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., & Soricut, R. (2019) · arXiv · doi:10.48550/arxiv.1909.11942 · Cited by 4,308
The Power of Scale for Parameter-Efficient Prompt Tuning
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Metadata
- Authors
- Kudo, T., & Richardson, J.
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
- Published
- 2018
- Pages
- 66-71
- OpenAlex
- W2885185669
- Cited by
- 2,869
- References
- 4
- FWCI
- 126.09
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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