Akiko Eriguchi

982 total citations
12 papers, 320 citations indexed

About

Akiko Eriguchi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Akiko Eriguchi has authored 12 papers receiving a total of 320 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 10 papers in Computer Vision and Pattern Recognition and 1 paper in Molecular Biology. Recurrent topics in Akiko Eriguchi's work include Natural Language Processing Techniques (12 papers), Topic Modeling (12 papers) and Multimodal Machine Learning Applications (10 papers). Akiko Eriguchi is often cited by papers focused on Natural Language Processing Techniques (12 papers), Topic Modeling (12 papers) and Multimodal Machine Learning Applications (10 papers). Akiko Eriguchi collaborates with scholars based in Japan, United States and United Kingdom. Akiko Eriguchi's co-authors include Yoshimasa Tsuruoka, Kazuma Hashimoto, Kyunghyun Cho, Hany Hassan, Yilin Yang, Kaori Abe, Toshiaki Nakazawa, Hideki Nakayama, Ondřej Bojar and Prasad Tadepalli and has published in prestigious journals such as Future Generation Computer Systems, Computational Linguistics and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.

In The Last Decade

Akiko Eriguchi

12 papers receiving 283 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Akiko Eriguchi Japan 6 305 153 35 14 8 12 320
Jindřich Helcl Czechia 9 282 0.9× 126 0.8× 12 0.3× 11 0.8× 11 1.4× 16 304
Huda Khayrallah United States 8 263 0.9× 114 0.7× 17 0.5× 8 0.6× 7 0.9× 17 284
Shuangzhi Wu China 12 345 1.1× 125 0.8× 29 0.8× 8 0.6× 9 1.1× 26 367
Yujie Zhang China 10 302 1.0× 58 0.4× 30 0.9× 21 1.5× 9 1.1× 50 320
Eunah Cho Germany 13 441 1.4× 91 0.6× 26 0.7× 14 1.0× 10 1.3× 41 463
Maria Nădejde United Kingdom 10 369 1.2× 112 0.7× 25 0.7× 21 1.5× 12 1.5× 21 391
Wei-Jen Ko United States 5 196 0.6× 84 0.5× 38 1.1× 13 0.9× 3 0.4× 10 231
Ohad Rubin Israel 2 211 0.7× 60 0.4× 34 1.0× 8 0.6× 3 240
Joern Wuebker Germany 11 415 1.4× 87 0.6× 12 0.3× 42 3.0× 7 0.9× 31 455
Akshar Bharati India 9 337 1.1× 60 0.4× 20 0.6× 8 0.6× 44 5.5× 19 353

Countries citing papers authored by Akiko Eriguchi

Since Specialization
Citations

This map shows the geographic impact of Akiko Eriguchi'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 Akiko Eriguchi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Akiko Eriguchi more than expected).

Fields of papers citing papers by Akiko Eriguchi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Akiko Eriguchi. 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 Akiko Eriguchi. The network helps show where Akiko Eriguchi may publish in the future.

Co-authorship network of co-authors of Akiko Eriguchi

This figure shows the co-authorship network connecting the top 25 collaborators of Akiko Eriguchi. A scholar is included among the top collaborators of Akiko Eriguchi 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 Akiko Eriguchi. Akiko Eriguchi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

12 of 12 papers shown
1.
Eriguchi, Akiko, Shufang Xie, Tao Qin, & Hany Hassan. (2022). Building Multilingual Machine Translation Systems That Serve Arbitrary XY Translations. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 600–606. 2 indexed citations
2.
Yang, Yilin, Akiko Eriguchi, Alexandre Muzio, et al.. (2021). Improving Multilingual Translation by Representation and Gradient Regularization. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 7266–7279. 12 indexed citations
3.
Nakazawa, Toshiaki, Hideki Nakayama, Chenchen Ding, et al.. (2021). Overview of the 8th Workshop on Asian Translation. 1–45. 34 indexed citations
4.
Hashimoto, Kazuma, et al.. (2021). Neural Text Generation with Artificial Negative Examples to Address Repeating and Dropping Errors. Journal of Natural Language Processing. 28(3). 751–777. 2 indexed citations
5.
Eriguchi, Akiko, Kazuma Hashimoto, & Yoshimasa Tsuruoka. (2019). Incorporating Source-Side Phrase Structures into Neural Machine Translation. Computational Linguistics. 45(2). 267–292. 9 indexed citations
6.
Eriguchi, Akiko, et al.. (2019). Combining Translation Memory with Neural Machine Translation. 123–130. 1 indexed citations
7.
Hashimoto, Kazuma, Akiko Eriguchi, Haixia Wang, et al.. (2018). Parallelizing and optimizing neural Encoder–Decoder models without padding on multi-core architecture. Future Generation Computer Systems. 108. 1206–1213. 5 indexed citations
8.
Eriguchi, Akiko, Yoshimasa Tsuruoka, & Kyunghyun Cho. (2017). Learning to Parse and Translate Improves Neural Machine Translation. 72–78. 80 indexed citations
9.
Hashimoto, Kazuma, Akiko Eriguchi, Haixia Wang, et al.. (2017). Cache Friendly Parallelization of Neural Encoder-Decoder Models Without Padding on Multi-core Architecture. 2. 437–440. 3 indexed citations
10.
Eriguchi, Akiko, Kazuma Hashimoto, & Yoshimasa Tsuruoka. (2016). Character-based Decoding in Tree-to-Sequence Attention-based Neural Machine Translation. International Conference on Computational Linguistics. 175–183. 8 indexed citations
11.
Hashimoto, Kazuma, Akiko Eriguchi, & Yoshimasa Tsuruoka. (2016). Domain Adaptation and Attention-Based Unknown Word Replacement in Chinese-to-Japanese Neural Machine Translation.. International Conference on Computational Linguistics. 75–83. 4 indexed citations
12.
Eriguchi, Akiko, Kazuma Hashimoto, & Yoshimasa Tsuruoka. (2016). Tree-to-Sequence Attentional Neural Machine Translation. 823–833. 160 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.

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