Self-explainable and fast-to-train example-based machine translation using neural networks
| Project/Area Number |
18K11447
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 61030:Intelligent informatics-related
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| Research Institution | Waseda University |
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Principal Investigator |
LEPAGE YVES 早稲田大学, 理工学術院(情報生産システム研究科・センター), 教授 (70573608)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2020: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2019: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | 自然言語処理 / 機械翻訳 / 用例手法 / ニューラルと統計手法 / analogy / machine translation / natural language / case-based reasoning / explainable AI |
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| Outline of Final Research Achievements |
This research introduced self-explanation in example-based machine translation (EBMT) by analogy. It is thus positioned in explainable artificial intelligence (XAI). Self-explanation was implemented by tracing the analogies verified or solved during translation. The direct and indirect approaches to EBMT by analogy were merged in system that uses an original neural network. It was studied how to retrieve sentences that cover a given sentence semantically and formally was built. It was studied how dense corpora are relative to analogies. Datasets of analogies between sentences were released.
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| Academic Significance and Societal Importance of the Research Achievements |
統計的機械翻訳（SMT）の手法である文部分アライメントと、ニューラル自然言語処理（NMT）の手法である単語や文のベクトル表現を用いて、類推関係方程式の解を求める手法を改善した。類推関係に基づいた用例機械翻訳の直接的なアプローチと間接的なアプローチを融合し、独自のニューラルネットワークを用いたシステムを構築した。入力は、単語のベクトル表現に基づく、単言語または対言語のソフトアライメントです。
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Report
(4 results)
Research Products
(11 results)
