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20172025
most citedChinese-Japanese Unsupervised Neural Machine Translation Using Sub-character Level Information

11 citations · 66 across the 25 of their papers we have counts for

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Showing 2018 · cs.CLShow all

5 papers · 2 filters

cs.CL2018

The Rule of Three: Abstractive Text Summarization in Three Bullet Points

Tomonori Kodaira, Mamoru Komachi

Neural network-based approaches have become widespread for abstractive text summarization. Though previously proposed models for abstractive text summarization addressed the proble…

cs.CL2018

Neural Machine Translation of Logographic Languages Using Sub-character Level Information

Longtu Zhang, Mamoru Komachi

Recent neural machine translation (NMT) systems have been greatly improved by encoder-decoder models with attention mechanisms and sub-word units. However, important differences be…

cs.CL2018

Graph-based Filtering of Out-of-Vocabulary Words for Encoder-Decoder Models

Satoru Katsumata, Yukio Matsumura, Hayahide Yamagishi +1

Encoder-decoder models typically only employ words that are frequently used in the training corpus to reduce the computational costs and exclude noise. However, this vocabulary set…

cs.CL2018

Japanese Predicate Conjugation for Neural Machine Translation

Michiki Kurosawa, Yukio Matsumura, Hayahide Yamagishi +1

Neural machine translation (NMT) has a drawback in that can generate only high-frequency words owing to the computational costs of the softmax function in the output layer. In Japa…

cs.CL2018

Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations

Hiroki Shimanaka, Tomoyuki Kajiwara, Mamoru Komachi

Sentence representations can capture a wide range of information that cannot be captured by local features based on character or word N-grams. This paper examines the usefulness of…