This map shows the geographic impact of Taro Watanabe's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Taro Watanabe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Taro Watanabe more than expected).
This network shows the impact of papers produced by Taro Watanabe. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Taro Watanabe. The network helps show where Taro Watanabe may publish in the future.
Co-authorship network of co-authors of Taro Watanabe
This figure shows the co-authorship network connecting the top 25 collaborators of Taro Watanabe.
A scholar is included among the top collaborators of Taro Watanabe based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Taro Watanabe. Taro Watanabe is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Watanabe, Taro, et al.. (2017). Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers). International Joint Conference on Natural Language Processing. 1.76 indexed citations
10.
Oda, Yusuke, Taku Kudo, Tetsuji Nakagawa, & Taro Watanabe. (2016). Phrase-based Machine Translation using Multiple Preordering Candidates. International Conference on Computational Linguistics. 1419–1428.
11.
Wu, Youzheng, Taro Watanabe, & Chiori Hori. (2014). Recurrent Neural Network-based Tuple Sequence Model for Machine Translation. International Conference on Computational Linguistics. 1908–1917.3 indexed citations
12.
Liu, Lemao, Tiejun Zhao, Taro Watanabe, & Eiichiro Sumita. (2013). Tuning SMT with a Large Number of Features via Online Feature Grouping. International Joint Conference on Natural Language Processing. 279–285.2 indexed citations
13.
Tamura, Akihiro, Taro Watanabe, & Eiichiro Sumita. (2012). Bilingual Lexicon Extraction from Comparable Corpora Using Label Propagation. Empirical Methods in Natural Language Processing. 24–36.42 indexed citations
14.
Neubig, Graham, Taro Watanabe, & Shinsuke Mori. (2012). Inducing a Discriminative Parser to Optimize Machine Translation Reordering. Empirical Methods in Natural Language Processing. 843–853.38 indexed citations
15.
Liu, Lemao, et al.. (2012). Locally Training the Log-Linear Model for SMT. Empirical Methods in Natural Language Processing. 402–411.15 indexed citations
16.
Neubig, Graham, Taro Watanabe, Eiichiro Sumita, Shinsuke Mori, & Tatsuya Kawahara. (2011). An Unsupervised Model for Joint Phrase Alignment and Extraction. Meeting of the Association for Computational Linguistics. 632–641.47 indexed citations
17.
Watanabe, Taro, et al.. (2010). The NICT translation system for IWSLT 2010.. IWSLT. 139–146.2 indexed citations
18.
Hayashi, Katsuhiko, Taro Watanabe, Hajime Tsukada, & Hideki Isozaki. (2009). Structural Support Vector Machines for Log-Linear Approach in Statistical Machine Translation. IWSLT. 144–151.4 indexed citations
19.
Watanabe, Taro, Jun Suzuki, Katsuhito Sudoh, Hajime Tsukada, & Hideki Isozaki. (2007). Larger Feature Set Approach for Machine Translation in IWSLT 2007. IWSLT. 111–118.1 indexed citations
20.
Tsukada, Hajime, Taro Watanabe, Jun Suzuki, Hideto Kazawa, & Hideki Isozaki. (2005). The NTT Statistical Machine Translation System for IWSLT2005. IWSLT. 112–117.4 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.