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: Pre-Translation for Neural Machine Transl...
Search
Yumeto Inaoka
June 21, 2017
Technology
130
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
文献紹介: Pre-Translation for Neural Machine Translation
2017/06/21の文献紹介で発表
Yumeto Inaoka
June 21, 2017
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
300
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
240
文献紹介: Beyond BLEU: Training Neural Machine Translation with Semantic Similarity
yumeto
0
340
文献紹介: EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing
yumeto
0
440
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
310
Other Decks in Technology
See All in Technology
セキュリティ専任チームなしで攻撃を先回りするSREが実践したSIEM on Amazon OpenSearch Service
cscengineer
PRO
0
690
Metadata - Section 3 : Dublin Core Standard · DC
mohab1
0
120
Shared Signals Framework 仕様解説
oidfj
0
280
evcc & Home Assistant
naltatis
0
300
Kiro 入門 (1 つのエージェントを、どの画面からでも) | 目指せ Kiro マスター 01
inariku
PRO
6
1.3k
Kiro Crew 入門 (手を離しても進む仕事) | 目指せ Kiro マスター 11
inariku
PRO
4
630
慶應義塾大学 AI実践05 論文執筆の仕組み化
keio_smilab
PRO
7
3.8k
並列度を下げたら速くなった — go build の重複コンパイルを実測で見る
tomoaki25
2
970
DBTS2026 盛り上がってますか!?Databricksが実現するAI時代のデータ基盤
akuwano
1
200
現場を動かすAIエージェント#2_製造業現場でのAI活用から見た、ハマる罠と実践知
kintotechdev
2
200
Copilot Coworkで「みのるん式」やってみた
minorun365
PRO
2
1.6k
長い仕事を任せる (Workflows、クラウドセッション、Kiro Web) | 目指せ Kiro マスター 10
inariku
PRO
3
140
Featured
See All Featured
Everyday Curiosity
cassininazir
0
400
Collaborative Software Design: How to facilitate domain modelling decisions
baasie
1
370
Leveraging LLMs for student feedback in introductory data science courses - posit::conf(2025)
minecr
1
470
Stop Working from a Prison Cell
hatefulcrawdad
274
21k
The Illustrated Children's Guide to Kubernetes
chrisshort
51
53k
Noah Learner - AI + Me: how we built a GSC Bulk Export data pipeline
techseoconnect
PRO
0
480
Testing 201, or: Great Expectations
jmmastey
46
8.4k
First, design no harm
axbom
PRO
2
1.4k
Visualizing Your Data: Incorporating Mongo into Loggly Infrastructure
mongodb
50
10k
CoffeeScript is Beautiful & I Never Want to Write Plain JavaScript Again
sstephenson
162
16k
The Limits of Empathy - UXLibs8
cassininazir
2
770
Digital Projects Gone Horribly Wrong (And the UX Pros Who Still Save the Day) - Dean Schuster
uxyall
1
3.8k
Transcript
1SF5SBOTMBUJPOGPS /FVSBM.BDIJOF5SBOTMBUJPO จݙհ ࣗવݴޠॲཧݚڀࣨɹҴԬເਓ +BO/JFIVFT &VOBI$IP 5IBOI-F)BBOE"MFY8BJCFM 1SPDFFEJOHTPG$0-*/(
QQ
֓ཁ ˗ /.5࣌ʹશ͘ҟͳΔҙຯͷ༁Λੜ ˡ·Εͳ୯ޠ͕͋Δ߹ʹݦஶ ˗ 1#.5ʹΑΔࣄલ༁Ͱతݴޠʹ༁ ˡ্هΛճආ ˗ /.5ʹೖྗจͱࣄલ༁ͷ݁ՌΛೖྗ ˠ#-&6είΞͷ্
/.5ͱ1#.5 ˗ /.5 ·Εͳ୯ޠͷ༁ʹࣦഊ ྲྀெͰจ๏తʹਖ਼͍͠༁Λੜ ˗ 1#.5 ·Εͳ୯ޠͰ༁͕Մೳ
ύΠϓϥΠϯʹΑΔ݁߹ ˗ 1#.5Ͱೖྗݴޠ͔ΒతݴޠF`ʹ༁ ˗ ্ͷग़ྗΛ/.5ͰΑΓྑ͍తจFʹ༁ ˗ 1#.5͕/.5ͰϦΧόʔͰ͖ͳ͍ΤϥʔΛ Ҿ͖ى͜͢Մೳੑ
/.5ͷࠞ߹ೖྗ ˗ /.5ʹ1#.5ͷग़ྗͱݪจΛೖྗ ˗ 1#.5ͷग़ྗޠኮͱݪจͷޠኮͷॏෳΛࢭ ˗ ͦΕͧΕͷೖྗΛ࿈݁ DPODBUFOBUJPO
࣮ݧ ˗ 8.5ͷӳಠχϡʔε༁λεΫͰੳ ˗ #-&6Ͱ༁࣭Λੳ ˗ ୯ޠͷසʹԠͯ͡ύϑΥʔϚϯεΛੳ ˗ 1#.5ͷ༁࣭ʹΑΔӨڹΛੳ͢ΔͨΊ ϕʔεϥΠϯγεςϜͱߴͳϞσϧΛ༻
˗ /.5ϞσϧͷγεςϜͱϞσϧͷ ΞϯαϯϒϧγεςϜΛ༻
࣮ݧ ˗ 1#.5 5FSFTB)FSSNBOO +BO/JFIVFT BOE"MFY8BJCFM $PNCJOJOH8PSE3FPSEFSJOH.FUIPETPOEJ⒎FSFOU-JOHVJTUJD"CTUSBDUJPO -FWFMTGPS4UBUJTUJDBM.BDIJOF5SBOTMBUJPO *O1SPDFFEJOHTPGUIF4FWFOUI8PSLTIPQPO4ZOUBY 4FNBOUJDTBOE
4USVDUVSFJO4UBUJTUJDBM5SBOTMBUJPO "MUBOUB (FPSHJB 64" ˗ /.5 3JDP4FOOSJDI #BSSZ)BEEPX BOE"MFYBOESB#JSDI /FVSBMNBDIJOFUSBOTMBUJPOPGSBSFXPSETXJUITVCXPSEVOJUT *O1SPDFFEJOHTPGUIFUI"OOVBM.FFUJOHPGUIF"TTPDJBUJPOGPS $PNQVUBUJPOBM-JOHVJTUJDT "$- "VHVTU #FSMJO (FSNBOZ 7PMVNF-POH1BQFST
݁Ռ #-&6είΞ ˗ ύΠϓϥΠϯ/.5ΑΓੑೳ͕ྼΔ ˠ1#.5ͷΤϥʔΛճ෮Ͱ͖ͳ͍ͨΊ ˗ ࠞ߹Ͱੑೳ্
݁Ռ ྫจ ˗ 1#.5Ͱ୯ޠͷ༁ਖ਼͍͕͠จ๏ʹΤϥʔ ˗ /.5୯ޠͷ༁ͰΤϥʔ ˗ ఏҊख๏Ͱ྆ํ͕վળ
݁Ռ ྫจ ˗ ೖྗจʹසޠSJPUؚ͕·ΕΔ ˗ 1#.5Ͱਖ਼͘͠"VGSVISʹ༁͞ΕΔ ˗ /.5ͦͷ··ग़ྗ ˗ ఏҊख๏Ͱਖ਼͘͠༁
·ͱΊ ˗ 1#.5ͱ/.5ΛΈ߹Θͤͨख๏ΛఏҊ ˗ ྆ϞσϧΛྻͰΈ߹Θͤͯ༏Εͨ༁ ੜͰ͖ͳ͍͕ೖྗΛࠞ߹͢ΔͱվળͰ͖Δ ˗ 1#.5ͷग़ྗͱݪจΛγϯϓϧʹ࿈݁ DPODBUFOBUJPO ͢Δ͜ͱͰ্࣭͕
˗ ݸʑΛվળ͢Δ͜ͱͰγεςϜશମͷ ύϑΥʔϚϯε্͕