1 code implementation • Findings (EMNLP) 2021 • Shoetsu Sato, Naoki Yoshinaga, Masashi Toyoda, Masaru Kitsuregawa
Our method chooses the most probable one from redundantly sampled latent variables for tying up the variable with a given response.
no code implementations • LREC 2022 • Fumikazu Sato, Naoki Yoshinaga, Masaru Kitsuregawa
In this study, to improve the accuracy of pronunciation prediction, we construct two large-scale Japanese corpora that annotate kanji characters with their pronunciations.
1 code implementation • 7 Apr 2023 • Shusuke Takahama, Yusuke Kurose, Yusuke Mukuta, Hiroyuki Abe, Akihiko Yoshizawa, Tetsuo Ushiku, Masashi Fukayama, Masanobu Kitagawa, Masaru Kitsuregawa, Tatsuya Harada
We conducted experiments on the pathological image dataset we created for this study and showed that the proposed method significantly improves the classification performance compared to existing methods.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Shoetsu Sato, Jin Sakuma, Naoki Yoshinaga, Masashi Toyoda, Masaru Kitsuregawa
Prior to fine-tuning, our method replaces the embedding layers of the NMT model by projecting general word embeddings induced from monolingual data in a target domain onto a source-domain embedding space.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Nobukazu Fukuda, Naoki Yoshinaga, Masaru Kitsuregawa
In this study, inspired by the processes for creating words from known words, we propose a robust method of estimating oov word embeddings by referring to pre-trained word embeddings for known words with similar surfaces to target oov words.
no code implementations • EMNLP (NLP-COVID19) 2020 • Akiko Aizawa, Frederic Bergeron, Junjie Chen, Fei Cheng, Katsuhiko Hayashi, Kentaro Inui, Hiroyoshi Ito, Daisuke Kawahara, Masaru Kitsuregawa, Hirokazu Kiyomaru, Masaki Kobayashi, Takashi Kodama, Sadao Kurohashi, Qianying Liu, Masaki Matsubara, Yusuke Miyao, Atsuyuki Morishima, Yugo Murawaki, Kazumasa Omura, Haiyue Song, Eiichiro Sumita, Shinji Suzuki, Ribeka Tanaka, Yu Tanaka, Masashi Toyoda, Nobuhiro Ueda, Honai Ueoka, Masao Utiyama, Ying Zhong
The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education.
no code implementations • 30 Apr 2020 • Shoetsu Sato, Jin Sakuma, Naoki Yoshinaga, Masashi Toyoda, Masaru Kitsuregawa
Prior to fine-tuning, our method replaces the embedding layers of the NMT model by projecting general word embeddings induced from monolingual data in a target domain onto a source-domain embedding space.
no code implementations • NAACL 2019 • Shonosuke Ishiwatari, Hiroaki Hayashi, Naoki Yoshinaga, Graham Neubig, Shoetsu Sato, Masashi Toyoda, Masaru Kitsuregawa
When reading a text, it is common to become stuck on unfamiliar words and phrases, such as polysemous words with novel senses, rarely used idioms, internet slang, or emerging entities.
1 code implementation • 1 Nov 2018 • Shonosuke Ishiwatari, Hiroaki Hayashi, Naoki Yoshinaga, Graham Neubig, Shoetsu Sato, Masashi Toyoda, Masaru Kitsuregawa
When reading a text, it is common to become stuck on unfamiliar words and phrases, such as polysemous words with novel senses, rarely used idioms, internet slang, or emerging entities.
no code implementations • ACL 2017 • Shonosuke Ishiwatari, JingTao Yao, Shujie Liu, Mu Li, Ming Zhou, Naoki Yoshinaga, Masaru Kitsuregawa, Weijia Jia
The chunk-level decoder models global dependencies while the word-level decoder decides the local word order in a chunk.
no code implementations • COLING 2016 • Tatsuya Iwanari, Kohei Ohara, Naoki Yoshinaga, Nobuhiro Kaji, Masashi Toyoda, Masaru Kitsuregawa
Kotonush, a system that clarifies people{'}s values on various concepts on the basis of what they write about on social media, is presented.