Hiroki Asano


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The AIP-Tohoku System at the BEA-2019 Shared Task
Hiroki Asano | Masato Mita | Tomoya Mizumoto | Jun Suzuki
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

We introduce the AIP-Tohoku grammatical error correction (GEC) system for the BEA-2019 shared task in Track 1 (Restricted Track) and Track 2 (Unrestricted Track) using the same system architecture. Our system comprises two key components: error generation and sentence-level error detection. In particular, GEC with sentence-level grammatical error detection is a novel and versatile approach, and we experimentally demonstrate that it significantly improves the precision of the base model. Our system is ranked 9th in Track 1 and 2nd in Track 2.


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Suspicious News Detection Using Micro Blog Text
Tsubasa Tagami | Hiroki Ouchi | Hiroki Asano | Kazuaki Hanawa | Kaori Uchiyama | Kaito Suzuki | Kentaro Inui | Atsushi Komiya | Atsuo Fujimura | Ryo Yamashita | Hitofumi Yanai | Akinori Machino
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation


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Reference-based Metrics can be Replaced with Reference-less Metrics in Evaluating Grammatical Error Correction Systems
Hiroki Asano | Tomoya Mizumoto | Kentaro Inui
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

In grammatical error correction (GEC), automatically evaluating system outputs requires gold-standard references, which must be created manually and thus tend to be both expensive and limited in coverage. To address this problem, a reference-less approach has recently emerged; however, previous reference-less metrics that only consider the criterion of grammaticality, have not worked as well as reference-based metrics. This study explores the potential of extending a prior grammaticality-based method to establish a reference-less evaluation method for GEC systems. Further, we empirically show that a reference-less metric that combines fluency and meaning preservation with grammaticality provides a better estimate of manual scores than that of commonly used reference-based metrics. To our knowledge, this is the first study that provides empirical evidence that a reference-less metric can replace reference-based metrics in evaluating GEC systems.