Grammatical Error Correction using Robust Word Representation Learning and Deep Neural Networks
| Project/Area Number |
16K16117
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Intelligent informatics
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| Research Institution | Tokyo Metropolitan University |
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Principal Investigator |
Komachi Mamoru 首都大学東京, システムデザイン研究科, 准教授 (60581329)
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| Research Collaborator |
Kaneko Masahiro
Zhang Longtu
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2017: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2016: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | 深層学習 / 単語分散表現 / 文法誤り訂正 / 深層言語表現 / 文法誤り検出 / 機械翻訳 / 誤り訂正 / 誤り検出 / 表現学習 / 分散表現 / LSTM / 第二言語習得 / マルチタスク学習 / 自然言語処理 / 機械学習 / ニューラネルットワーク |
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| Outline of Final Research Achievements |
This study proposed an approach to apply deep learning to grammatical error detection and correction. The sentences written by language learners differ from those written by native speakers in that they may make mistakes in the words themselves or in the context of which they are used. Taking these differences in context into account, we built a mathematical model for representing words and used deep learning to detect and correct grammatical errors. We also proposed a method for error detection using a contextualized language representation model learned from a large amount of text data written by native speakers, and achieved the state-of-the-art accuracy in English grammatical error detection.
On the other hand, for Japanese and Chinese, we proposed a model that reconstructs word sequence by decomposing kanji into radicals, and demonstrated its effectiveness in Japanese-Chinese neural machine translation.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究では英語学習者の文法誤り検出について、学習者の文章の誤り方を考慮して単語をモデル化することと、ネイティブが書いた大規模な文章データから獲得した文脈つきの言語表現モデルを用いることが、それぞれ有効であることを世界で初めて示し、いずれの研究においても当時の世界最高精度の精度を達成することができました。本研究は世界を代表する英語学習者の文法誤り検出の研究の一つです。
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Report
(4 results)
Research Products
(16 results)
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[Presentation] TMU System for SLAM-20182018
Author(s)
Masahiro Kaneko, Tomoyuki Kajiwara and Mamoru Komachi
Organizer
13th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2018): Shared Task on Second Language Acquisition Modeling. New Orleans, USA.
Related Report
Int'l Joint Research
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