Refining the state-of-the-art in Machine Translation, optimizing NMT for the JA <-> EN language pair by leveraging personal domain expertise
Not on the map. Why?
Abstract (arXiv)
Documenting the construction of an NMT (Neural Machine Translation) system for En/Ja based on the Transformer architecture leveraging the OpenNMT framework. A systematic exploration of corpora pre-processing, hyperparameter tuning and model architecture is carried out to obtain optimal performance. The system is evaluated using standard auto-evaluation metrics such as BLEU, and my subjective opinion as a Japanese linguist.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 29 references that are in the map. First posted on arXiv 23 Feb 2022.
Builds on
12 of 29 references.
References · 12
- 2017Attention Is All You Needcited by 59,932
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
- 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
- 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
- 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,024
- 2018Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidatescited by 632
- 2019BPE-Dropout: Simple and Effective Subword Regularizationcited by 189
- 2016Long Short-Term Memory-Networks for Machine Readingcited by 161
- 2019Neural Machine Translation with Byte-Level Subwordscited by 115
- 2019Synchronous Bidirectional Neural Machine Translationcited by 27
- 2019Shared-Private Bilingual Word Embeddings for Neural Machine Translationcited by 9
Built on by
Not cited yet.