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
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
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
300
Other Decks in Technology
See All in Technology
自作バイナリを動かす「入口」8選
puhitaku
1
900
AI Made Us Faster at Solving the Wrong Problems
marceloancelmo
0
190
The seven pitfalls of AI (revised version)
ufried
0
290
freeeらしさをAIとともに作る / Creating the freee Experience with AI
ymrl
0
210
プロダクト価値を、 チームが使える判断軸に変える
vivion
0
130
FinTech 1-2 : Overview of FinTech
ks91
PRO
0
150
オブザーバビリティを高める AI エージェント体験を考える / Designing AI Agent Experiences That Enhance Observability
aoto
PRO
2
320
OpenSharing について熱く語る〜AI アセットの共有について〜
kameitomohiro
0
240
今さら理解するウェブフォントver.2026
takanorip
2
290
Execution in the Kingdom of Agents: Reflections on Abstraction and Complexity
bcantrill
0
880
"優秀な誰か"の下位互換にならないために
yut0naga1_fa
0
140
使いこなすために知っておきたい Azure SRE Agent アンチパターン
torumakabe
2
410
Featured
See All Featured
世界の人気アプリ100個を分析して見えたペイウォール設計の心得
akihiro_kokubo
PRO
74
42k
The Spectacular Lies of Maps
axbom
PRO
1
1.1k
The Web Performance Landscape in 2024 [PerfNow 2024]
tammyeverts
12
1.3k
Marketing to machines
jonoalderson
1
5.9k
Redefining SEO in the New Era of Traffic Generation
szymonslowik
1
460
Primal Persuasion: How to Engage the Brain for Learning That Lasts
tmiket
0
490
Lessons Learnt from Crawling 1000+ Websites
charlesmeaden
PRO
1
1.6k
Discover your Explorer Soul
emna__ayadi
2
1.3k
Taking LLMs out of the black box: A practical guide to human-in-the-loop distillation
inesmontani
PRO
3
2.4k
ReactJS: Keep Simple. Everything can be a component!
pedronauck
666
130k
Stop Working from a Prison Cell
hatefulcrawdad
274
21k
We Analyzed 250 Million AI Search Results: Here's What I Found
joshbly
2
2k
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