| Data augmentation using back-translation for context-aware neural machine translation A Sugiyama, N Yoshinaga DiscoMT 2019, 35-44, 2019 | 152 | 2019 |
| Predicting and Eliciting Addressee's Emotion in Online Dialogue T Hasegawa, N Kaji, N Yoshinaga, M Toyoda ACL 2013 (long), 964-972, 2013 | 94 | 2013 |
| Learning to Describe Unknown Phrases with Local and Global Contexts S Ishiwatari, H Hayashi, N Yoshinaga, G Neubig, S Sato, M Toyoda, ... NAACL 2019 (oral), 3467-3476, 2019 | 80 | 2019 |
| A bag of useful tricks for practical neural machine translation: Embedding layer initialization and large batch size M Neishi, J Sakuma, S Tohda, S Ishiwatari, N Yoshinaga, M Toyoda WAT 2017, 99-109, 2017 | 71 | 2017 |
| Boosting Precision and Recall of Hyponymy Relation Acquisition from Hierarchical Layouts in Wikipedia A Sumida, N Yoshinaga, K Torisawa LREC 2008, 2008 | 68 | 2008 |
| On the Relation between Position Information and Sentence Length in Neural Machine Translation M Neishi, N Yoshinaga CoNLL 2019, 328-338, 2019 | 61 | 2019 |
| Modeling User Leniency and Product Popularity for Sentiment Classification W Gao, N Yoshinaga, N Kaji, M Kitsuregawa IJCNLP 2013, 1107-1111, 2013 | 57 | 2013 |
| Open-domain attribute-value acquisition from semi-structured texts N Yoshinaga, K Torisawa OntoLex 2007: ISWC 2007 Workshop, 55-66, 2007 | 55 | 2007 |
| A Self-adaptive Classifier for Efficient Text-stream Processing N Yoshinaga, M Kitsuregawa COLING 2014, 1091-1102, 2014 | 48 | 2014 |
| Vocabulary adaptation for domain adaptation in neural machine translation S Sato, J Sakuma, N Yoshinaga, M Toyoda, M Kitsuregawa EMNLP 2020 (Findings), 4269-4279, 2020 | 47 | 2020 |
| Analysis and visualization of temporal changes in bloggers' activities and interests M Itoh, N Yoshinaga, M Toyoda, M Kitsuregawa IEEE PacificVis 2012, 57-64, 2012 | 47 | 2012 |
| Modeling situations in neural chat bots S Sato, N Yoshinaga, M Toyoda, M Kitsuregawa ACL 2017 SRW, 120-127, 2017 | 41 | 2017 |
| Sentiment classification in under-resourced languages using graph-based semi-supervised learning methods Y Ren, N Kaji, N Yoshinaga, M Kitsuregawa IEICE Transactions on Information and Systems 97 (4), 790-797, 2014 | 36 | 2014 |
| Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction K Shinzato, N Yoshinaga, Y Xia, WT Chen ACL 2022 (oral), 227-234, 2022 | 34 | 2022 |
| Evolution of public opinion on COVID-19 vaccination in Japan: large-scale Twitter data analysis R Kobayashi, Y Takedomi, Y Nakayama, T Suda, T Uno, T Hashimoto, ... Journal of Medical Internet Research 24 (12), e41928, 2022 | 33 | 2022 |
| Kernel slicing: Scalable online training with conjunctive features N Yoshinaga, M Kitsuregawa COLING 2010 (oral), 1245-1253, 2010 | 31 | 2010 |
| Efficient staggered decoding for sequence labeling N Kaji, Y Fujiwara, N Yoshinaga, M Kitsuregawa ACL 2010, 485-494, 2010 | 28 | 2010 |
| Polynomial to linear: Efficient classification with conjunctive features N Yoshinaga, M Kitsuregawa EMNLP 2009, 1542-1551, 2009 | 28 | 2009 |
| Chunk-based decoder for neural machine translation S Ishiwatari, J Yao, S Liu, M Li, M Zhou, N Yoshinaga, M Kitsuregawa, ... ACL 2017 (long), 1901-1912, 2017 | 26 | 2017 |
| Sentiment Classification in Resource-Scarce Languages by using Label Propagation Y Ren, N Kaji, N Yoshinaga, M Toyoda, M Kitsuregawa PACLIC 2011, 420-429, 2011 | 23 | 2011 |
Masashi ToyodaInstitute of Industrial Science, The University of TokyoVerified email at acm.org
