Papers by Jin Sakuma
Vocabulary Adaptation for Domain Adaptation in Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | Neural network methods exhibit strong performance only in a few resource-rich domains. |
| Approach: | They propose a method that fine-tunes embedding layers of a pre-trained NMT model to the target domain. |
| Outcome: | The proposed method improves fine-tuning performance in En-Ja and De-En translation by 3.86 and 3.28 BLEU points. |
Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia (2020.emnlp-demos)
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Ikuya Yamada, Akari Asai, Jin Sakuma, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji, Yuji Matsumoto
| Challenge: | Existing tools for learning the embeddings of words and entities from Wikipedia are not yet available. |
| Approach: | They propose a Python-based tool for learning Wikipedia embeddings from Wikipedia . they use a Wikipedia dump file as an argument to issue a single command . |
| Outcome: | The proposed tool achieves state-of-the-art results on the KORE entity relatedness dataset and competitive results on benchmark datasets. |