[PACLING2019] Improving Context-aware Neural Machine Translation with Target-...Hayahide Yamagishi
This is the slide used in the oral presentation at PACLING2019.
(For Japanese speakers) 本発表は私の修論発表と同等ですので、日本語がわかる方は以下のスライドの方が読みやすいかもしれません。
https://www.slideshare.net/HayahideYamagishi/ss-181147693/HayahideYamagishi/ss-181147693
Font has been changed the original one (Hiragino Maru Gothic Pro W4) into the other one by the SlideShare.
[ACL2017読み会] What do Neural Machine Translation Models Learn about Morphology?Hayahide Yamagishi
1) The document examines what neural machine translation models learn about morphology through experiments analyzing the hidden states of NMT models.
2) It finds that character-based word representations better capture morphological information than word-based representations, and that lower encoder layers learn more about a word's structure while higher layers improve translation.
3) The target language does not significantly impact how much the model learns about source language morphology, and decoder states do not capture rich morphological information.
The document discusses why neural machine translation (NMT) models are better than statistical machine translation (SMT) models at producing translations of the appropriate length. It shows that SMT models tended to generate shorter translations due to optimizing for BLEU score, while NMT models directly optimize for maximum likelihood and thus produce lengthier translations that match the source text. The document then demonstrates this concept using a toy copying task, where an NMT model is able to match the length of input strings during translation more accurately than SMT models.
A hierarchical neural autoencoder for paragraphs and documentsHayahide Yamagishi
The document describes 3 hierarchical LSTM models for generating coherent multi-sentence text:
1) Standard LSTM encodes/decodes a document as a single sequence.
2) Hierarchical LSTM encodes sentences then the document.
3) Hierarchical LSTM with attention encodes sentences then decodes with attention over encoded sentences.
The models were evaluated on hotel reviews and Wikipedia using ROUGE, BLEU, and a coherence metric called L-value. The hierarchical LSTMs outperformed the standard LSTM, and hotel reviews were easier to generate than Wikipedia text.
[PACLING2019] Improving Context-aware Neural Machine Translation with Target-...Hayahide Yamagishi
This is the slide used in the oral presentation at PACLING2019.
(For Japanese speakers) 本発表は私の修論発表と同等ですので、日本語がわかる方は以下のスライドの方が読みやすいかもしれません。
https://www.slideshare.net/HayahideYamagishi/ss-181147693/HayahideYamagishi/ss-181147693
Font has been changed the original one (Hiragino Maru Gothic Pro W4) into the other one by the SlideShare.
[ACL2017読み会] What do Neural Machine Translation Models Learn about Morphology?Hayahide Yamagishi
1) The document examines what neural machine translation models learn about morphology through experiments analyzing the hidden states of NMT models.
2) It finds that character-based word representations better capture morphological information than word-based representations, and that lower encoder layers learn more about a word's structure while higher layers improve translation.
3) The target language does not significantly impact how much the model learns about source language morphology, and decoder states do not capture rich morphological information.
The document discusses why neural machine translation (NMT) models are better than statistical machine translation (SMT) models at producing translations of the appropriate length. It shows that SMT models tended to generate shorter translations due to optimizing for BLEU score, while NMT models directly optimize for maximum likelihood and thus produce lengthier translations that match the source text. The document then demonstrates this concept using a toy copying task, where an NMT model is able to match the length of input strings during translation more accurately than SMT models.
A hierarchical neural autoencoder for paragraphs and documentsHayahide Yamagishi
The document describes 3 hierarchical LSTM models for generating coherent multi-sentence text:
1) Standard LSTM encodes/decodes a document as a single sequence.
2) Hierarchical LSTM encodes sentences then the document.
3) Hierarchical LSTM with attention encodes sentences then decodes with attention over encoded sentences.
The models were evaluated on hotel reviews and Wikipedia using ROUGE, BLEU, and a coherence metric called L-value. The hierarchical LSTMs outperformed the standard LSTM, and hotel reviews were easier to generate than Wikipedia text.
20. 首都大学東京主催 平成30年度情報通信システム学域修士論文発表会, 平成31年2月6日,首都大学東京日野キャンパス2号館 401・402教室 19
実験 文 (後処理済み、それぞれ上下が1文目と2文目に対応)
入力文
わかめはよく洗って塩を落とし、10分ほど水に浸けておいてからざく切りにする。
長ねぎは小口切りにする。
熱した鍋にごま油をひき、わかめと長ねぎを入れて30秒ほど軽く炒める。
参照訳
Wash the wakame well to remove the salt, put into a bowl of water for 10
minutes and drain. Cut into large pieces. Slice the Japanese leek.
Heat a pan and pour the sesame oil. Stir fry the wakame and leek for 30
seconds.
Baseline
Wash the wakame seaweed well and remove the salt. Soak in water for
10 minutes, then roughly chop. Cut the Japanese leek into small pieces.
Heat sesame oil in a heated pot, add the wakame and leek, and lightly
sauté for about 30 seconds.
Separated
Target
Wash the wakame well, soak in water for about 10 minutes. Cut into
small pieces. Cut the Japanese leek into small pieces.
Heat the sesame oil in a frying pan, add the wakame and leek, and
stir-fry for about 30 seconds.
21. 首都大学東京主催 平成30年度情報通信システム学域修士論文発表会, 平成31年2月6日,首都大学東京日野キャンパス2号館 401・402教室 20
実験 文 (後処理済み、それぞれ上下が1文目と2文目に対応)
入力文
わかめはよく洗って塩を落とし、10分ほど水に浸けておいてからざく切りにする。
長ねぎは小口切りにする。
熱した鍋にごま油をひき、わかめと長ねぎを入れて30秒ほど軽く炒める。
参照訳
Wash the wakame well to remove the salt, put into a bowl of water for 10
minutes and drain. Cut into large pieces. Slice the Japanese leek.
Heat a pan and pour the sesame oil. Stir fry the wakame and leek for 30
seconds.
Baseline
Wash the wakame seaweed well and remove the salt. Soak in water for
10 minutes, then roughly chop. Cut the Japanese leek into small pieces.
Heat sesame oil in a heated pot, add the wakame and leek, and lightly
sauté for about 30 seconds.
Shared
Target
Wash the wakame well, remove the salt, soak in water for about 10
minutes, then roughly chop. Chop the Japanese leek into small pieces.
Heat sesame oil in a heated pan, add the wakame and Japanese leek,
and lightly stir-fry for about 30 seconds.