Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Sign up for free
Menu
Search
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Pricing
Search
Sign in
Sign up for free
文献紹介: EditNTS: An Neural Programmer-Interpreter...
Search
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
Yumeto Inaoka
August 08, 2019
Research
440
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
文献紹介: EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing
2019/08/08の文献紹介で発表
Yumeto Inaoka
August 08, 2019
More Decks by Yumeto Inaoka
See All by Yumeto Inaoka
文献紹介: Quantity doesn’t buy quality syntax with neural language models
yumeto
1
230
文献紹介: Open Domain Web Keyphrase Extraction Beyond Language Modeling
yumeto
0
290
文献紹介: Self-Supervised_Neural_Machine_Translation
yumeto
0
200
文献紹介: Comparing and Developing Tools to Measure the Readability of Domain-Specific Texts
yumeto
0
220
文献紹介: PAWS: Paraphrase Adversaries from Word Scrambling
yumeto
0
220
文献紹介: Beyond BLEU: Training Neural Machine Translation with Semantic Similarity
yumeto
0
330
文献紹介: Decomposable Neural Paraphrase Generation
yumeto
0
260
文献紹介: Analyzing the Limitations of Cross-lingual Word Embedding Mappings
yumeto
0
300
文献紹介: Similarity-Based Reconstruction Loss for Meaning Representation
yumeto
1
250
Other Decks in Research
See All in Research
Evaluation génomique des femelles laitières croisées : comprendre la méthode pour accompagner les éleveurs à son utilisation
institudelelevage
PRO
0
100
Anthropic が提案する LLM の内部状態を自然言語で説明可能にした Natural Language Autoencoders / Natural Language Autoencoders Produce Unsupervised Explanations of LLM Activations
shunk031
0
280
適応的スパムフィルタのための軽量な類似メッセージカウンタ / jsai2026-adaptive-spam-filter
monochromegane
0
5.6k
ros2-perf-multihost: Automated Coordination Framework for Objective Architecture Evaluation in Distributed Systems
takasehideki
0
120
NLP colloquium: AI Safety Survey
kanekomasahiro
2
1.1k
高性能計算機クラスタを用いた大規模点群処理による森林の単木抽出と構造解析
kentaitakura
1
130
MIRU2026 チュートリアル講演2:三次元データ処理の動向
nnchiba
6
4.9k
IA for theory
gpeyre
1
440
La génomique au service de la fromageabilité du lait grâce aux spectres MIR
institudelelevage
PRO
0
110
PHTalks Bengaluru - SSRF When All Else Fails
dk999
0
1.2k
OWASP AISVS - C7
shiell
2
780
PGDM: Physically Guided Diffusion Model for L Downscaling
satai
3
490
Featured
See All Featured
Bash Introduction
62gerente
615
220k
A Modern Web Designer's Workflow
chriscoyier
699
190k
Automating Front-end Workflow
addyosmani
1369
210k
AI: The stuff that nobody shows you
jnunemaker
PRO
10
1.1k
The Art of Programming - Codeland 2020
erikaheidi
57
14k
Connecting the Dots Between Site Speed, User Experience & Your Business [WebExpo 2025]
tammyeverts
11
1k
コードの90%をAIが書く世界で何が待っているのか / What awaits us in a world where 90% of the code is written by AI
rkaga
63
46k
Practical Orchestrator
shlominoach
192
12k
Pawsitive SEO: Lessons from My Dog (and Many Mistakes) on Thriving as a Consultant in the Age of AI
davidcarrasco
0
250
The Success of Rails: Ensuring Growth for the Next 100 Years
eileencodes
47
8.3k
Chrome DevTools: State of the Union 2024 - Debugging React & Beyond
addyosmani
10
1.3k
Organizational Design Perspectives: An Ontology of Organizational Design Elements
kimpetersen
PRO
1
830
Transcript
None
None
• • • •
• •
None
None
• • •1:−1 •1:−1 •
• • enc = 1 , … , enc =
ℎ1 enc, … , ℎ enc • = 1 , … , •ℎ enc = LSTMenc 1 , 2 1 ∙ , 2 ∙
• •h enc •ℎ edit 1:−1 • •ℎ edit =
LSTMedit ℎ enc, , ℎ−1 edit, ℎ−1 int • = σ =1 || ℎ = softmax ℎ , ℎ
• •
• •ℎ int = LSTMint ℎ−1 int , −1
• •
• •
•
• • •
• • • •
• • • • •
• •
• •
• •
•
• • • •