Sho Hoshino


2022

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Aspect-based Analysis of Advertising Appeals for Search Engine Advertising
Soichiro Murakami | Peinan Zhang | Sho Hoshino | Hidetaka Kamigaito | Hiroya Takamura | Manabu Okumura
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track

Writing an ad text that attracts people and persuades them to click or act is essential for the success of search engine advertising. Therefore, ad creators must consider various aspects of advertising appeals (A3) such as the price, product features, and quality. However, products and services exhibit unique effective A3 for different industries. In this work, we focus on exploring the effective A3 for different industries with the aim of assisting the ad creation process. To this end, we created a dataset of advertising appeals and used an existing model that detects various aspects for ad texts. Our experiments demonstrated %through correlation analysis that different industries have their own effective A3 and that the identification of the A3 contributes to the estimation of advertising performance.

2015

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Discriminative Preordering Meets Kendall’s 𝜏 Maximization
Sho Hoshino | Yusuke Miyao | Katsuhito Sudoh | Katsuhiko Hayashi | Masaaki Nagata
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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Japanese to English Machine Translation using Preordering and Compositional Distributed Semantics
Sho Hoshino | Hubert Soyer | Yusuke Miyao | Akiko Aizawa
Proceedings of the 1st Workshop on Asian Translation (WAT2014)

2013

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Two-Stage Pre-ordering for Japanese-to-English Statistical Machine Translation
Sho Hoshino | Yusuke Miyao | Katsuhito Sudoh | Masaaki Nagata
Proceedings of the Sixth International Joint Conference on Natural Language Processing