further fine-tuned simplification operations. In particular, we show that neural machine translation can be effectively used in this situation. Previous applications of machine translation for simplification reveal that it has considerable disadvantage of being overly conservative, often failing to modify the source in any way. The proposed method of splitting based on semantic parsing alleviates this issue;. after splitting, more fine-tuned simplification operations can be applied to the text.” - 本研究で作成したデータセットより “Flow（論述の流れ）” ; この情報は論述の流れを中断 させるため下に移動させてください 論述全体の流れや⼀貫性など⽂書単位でライティングの品質を向上させること を⽬的とした編集
the noise generated.” “We present results of on a quantitative analysis.” ⽂書 句/⽂ 単語 ライティング “ After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural machine translation can be effectively used in this situation. Previous applications of machine translation for simplification reveal that it has considerable disadvantage of being overly conservative, often failing to modify the source in any way. The proposed method of splitting based on semantic parsing alleviates this issue;. after splitting, more fine-tuned simplification operations can be applied to the text.” Editing Proofreading 典型的なライティングプロセスは広範囲で⾼次な観点から徐々に編集 範囲を狭めていくとされている [Buchman et al., 2000; Seow, 2002] Revision スコープ
the noise generated.” “We present results of on a quantitative analysis.” ⽂書 句/⽂ 単語 ライティング “ After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural machine translation can be effectively used in this situation. Previous applications of machine translation for simplification reveal that it has considerable disadvantage of being overly conservative, often failing to modify the source in any way. The proposed method of splitting based on semantic parsing alleviates this issue;. after splitting, more fine-tuned simplification operations can be applied to the text.” Revision Editing Proofreading 前置詞や冠詞など閉じたクラスを対象とした局所的な編集から句・⽂単位で 流暢性のある編集へとスコープを広げてきた [Ng et al., 2014; Napoles et al., 2017] スコープ ⾃然⾔語処理 ⽂法誤り訂正 - 流暢性のある編集 - - 局所的な編集 - ⽂書単位で⾃動的にリビジョンを⾏う研究についてほとんど議論されていない
the noise generated.” “We present results of on a quantitative analysis.” ⽂書 句/⽂ 単語 “ After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural machine translation can be effectively used in this situation. Previous applications of machine translation for simplification reveal that it has considerable disadvantage of being overly conservative, often failing to modify the source in any way. The proposed method of splitting based on semantic parsing alleviates this issue;. after splitting, more fine-tuned simplification operations can be applied to the text.” Revision ʢOur focusʣ Editing Proofreading 本研究のビジョン: ⾼精度な⾃動リビジョンシステムの実現 →より広く深い⽂脈を考慮した編集技術が必要 ⽂法誤り訂正 - 流暢性のある編集 - - 局所的な編集 - 論述リビジョン ライティング スコープ ⾃然⾔語処理 本研究の⽬的: 論述リビジョンタスクの提案とそのための研究基盤の提供
Yahoo! Answers, OKWave and Baidu Zhidao, have become popular web services. In these services, a user posts a question and other users answer it. The questioner chooses one of the answers as the best answer. These services have many threads consisting of one question and a number of answers, and the number of threads grows day by day. The threads are stored and anyone can read them. When a user has a question, if there is a similar question in the service, he or she can refer to the answers to the similar question. Herefrom, these services are useful for not only the questioner but also other users having a similar question. Community-based Question Answering services, such as Yahoo! Answers, OKWave, and Baidu Zhidao, have become popular web services. As the name suggests, on such services, a user posts a question, other users answer it, and the original questioner selects the best answer. Typically, such services have an increasing number of threads comprising a single question and multiple answers. The threads are stored and are publicly available. If a user posts a question similar to one stored in the system, they can refer to the answers to the stored question. 出⼒⽂書 𝑑’ • ⼊⼒⽂書 𝑑 が与えられたとき，⾃動リビジョンシステム 𝒇 は元の意味を保持しつつ⽂書 単位で品質を向上させるためのリビジョン 𝑹 を⾏い出⼒⽂書 𝑑’ を返すタスク (𝑓: 𝑑 ⟼ 𝑑’) • リビジョン 𝑹 は編集 𝒆 の集合からなる (𝒆 ∈ 𝑹) システム 𝒇 編集 𝒆 編集 𝒆 リビジョン 𝑹
to address these shortcomings. Firstly, (…) Secondly, (…) Finally, the more layers we freeze the fewer layers we will need to back-propagate through during training. Thus we expect to see a decrease in GPU memory usage since we do not have to maintain gradients for all layers. We propose an approach combining two methodologies to address these shortcomings. Firstly, (…) Secondly, (…) Finally, the more layers we freeze the fewer layers we will need to back-propagate through during training; thus, we expect to see a decrease in GPU memory usage since we do not have to maintain gradients for all layers. “Clarity”; joining these two sentences to make it clear the both form the third improvement, rather than there being four. リビジョン前: リビジョン後:
and make the choice of "have" vs "has" more clear リビジョン前: リビジョン後: In this research area, image captioning methods, which automatically generate image descriptions (captions), have attracted a great deal of attention (Karpathy and Fei-Fei, 2015; Donahue et al., 2015; Vinyals et al., 2015; Mao et al., 2015). In this research area, methods to automatically generate image descriptions (captions), that is, image captioning, have attracted a great deal of attention (Karpathy and Fei-Fei, 2015; Donahue et al., 2015; Vinyals et al., 2015; Mao et al., 2015).
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