The DOI matches its Crossref record; 20 of the 21 references it cites were found in a registry or an index.
Neural Machine Translation of Logographic Language Using Sub-character Level Information
Open accessAbstract
Recent neural machine translation (NMT) systems have been greatly improved by encoder-decoder models with attention mechanisms and sub-word units.However, important differences between languages with logographic and alphabetic writing systems have long been overlooked.This study focuses on these differences and uses a simple approach to improve the performance of NMT systems utilizing decomposed sub-character level information for logographic languages.Our results indicate that our approach not only improves the translation capabilities of NMT systems between Chinese and English, but also further improves NMT systems between Chinese and Japanese, because it utilizes the shared information brought by similar sub-character units.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
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
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al. (2016) · arXiv · doi:10.48550/arxiv.1609.08144 · Cited by 5,319
Statistical Significance Tests for Machine Translation Evaluation.
Koehn, P. (2004) · Cited by 1,455
Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates
Kudo, T. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1007 · Cited by 1,021
Statistical significance tests.
Cox, D. (1982) · British Journal of Clinical Pharmacology · doi:10.1111/j.1365-2125.1982.tb01987.x · Cited by 335
ASPEC: Asian Scientific Paper Excerpt Corpus
Nakazawa, T., Yaguchi, M., Uchimoto, K., Utiyama, M., Sumita, E., Kurohashi, S., & Isahara, H. (2016) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3iku22jbpbzm · Cited by 166
Radical Embedding: Delving Deeper to Chinese Radicals
Shi, X., Zhai, J., Yang, X., Xie, Z., & Liu, C. (2015) · doi:10.3115/v1/p15-2098 · Cited by 80
Learning Character-level Compositionality with Visual Features
Liu, F., Han, L., Lo, C., & Neubig, G. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1188 · Cited by 65
Travatar: A Forest-to-String Machine Translation Engine based on Tree Transducers
Neubig, G. (2013) · Cited by 50
Radical-Based Hierarchical Embeddings for Chinese Sentiment Analysis at Sentence Level.
Peng, H., Cambria, E., & Zou, X. (2017) · The Florida AI Research Society · Cited by 32
Pinyin as Subword Unit for Chinese-Sourced Neural Machine Translation.
Du, J., & Way, A. (2017) · AICS · Cited by 13
Word, Subword or Character? An Empirical Study of Granularity in Chinese-English NMT
Wang, Y., Zhou, L., Zhang, J., & Zong, C. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-7134-8_4 · Cited by 12
Utilizing Visual Forms of Japanese Characters for Neural Review Classification.
Toyama, Y., Miwa, M., & Sasaki, Y. (2017) · International Joint Conference on Natural Language Processing · Cited by 7
Investigating Stroke-Level Information for Learning Chinese Word Embeddings.
Cao, S., Lu, W., Zhou, J., & Li, X. (2017) · ISWC (Posters, Demos & Industry Tracks) · Cited by 2
Apply Chinese Radicals Into Neural Machine Translation: Deeper Than Character LevelPreprint
Kuang, S., & Han, L. (2018) · arXiv (Cornell University) · Cited by 2
Research works printed but not linked: 2
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735-1780.
- Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Neural machine translation of rare words with subword units. In ACL 2016, pages 1715-1725.
Cited by
Sorted by citations
Koehn, P. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108608480 · Cited by 375
Comparison of text preprocessing methods
Chai, C. P. (2022) · Natural Language Engineering · doi:10.1017/s1351324922000213 · Cited by 303
“Researchers have attempted to mitigate these problems by borrowing information from a parallel corpus of another language, commonly in multilingual corpora for translation (Luo, Tinsley, and Lepage 2013; Zhang and Komachi 2018).”
BackgroundAn Efficient Method for Generating Synthetic Data for Low-Resource Machine Translation
Ngo, T.-V., Nguyen, P.-T., Nguyen, V. V., Ha, T.-L., & Nguyen, L.-M. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2022.2101755 · Cited by 30
“As an otherwise idea, Zhang et al. (2018) use a forward model to predict the translations in the target language – called Self-learning.”
BackgroundMethodInfluentialWhy don’t people use character-level machine translation?
Libovický, J., Schmid, H., & Fraser, A. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.194 · Cited by 35
“Zhang and Komachi (2018) argued that signs in logographic languages carry too much information and were able to improve the translation quality by segmenting Chinese and Japanese into sub-character units while keeping subword segmentation on the English side.”
BackgroundNguyen, M., Ngo, G. H., & Chen, N. F. (2019) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2019.2955246 · Cited by 23
“blem of out-of-vocabulary words and characters. However, alphabetic languages and languages with logographic origin are often treated the same way, disregarding their intrinsically marked differences [52]. Modeling logograph subunits can alleviate these issues since there are fewer subunits and they can be used to con…”
BackgroundInfluential
Metadata
- Authors
- Zhang, L., & Komachi, M.
- Affiliations
- Tokyo Metropolitan University (JP)
- Venue
- Proceedings of the Third Conference on Machine Translation: Research Papers
- Published
- 2018
- Pages
- 17-25
- OpenAlex
- W2963223057
- Cited by
- 53
- References
- 21
- Citations by year
- 2025: 6 · 2024: 3 · 2023: 3 · 2022: 6 · 2021: 1 · 2020: 27 · 2019: 5 · 2018: 2
- FWCI
- 3.57
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Machine Translation (WMT) (publishedVersion, cc-by)
- Funders
- Japan Society for the Promotion of Science (16K16117)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- neural machine translation, logographic languages, character decomposition, Chinese-English translation, Chinese-Japanese translation, subword units
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
