Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Learning Semantic Sentence Embeddings using Pai...
Search
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
onizuka laboratory
October 23, 2018
Research
0
120
Learning Semantic Sentence Embeddings using Pair-wise Discriminator
弊研究室で行なったCOLING2018読み会の発表資料です。
onizuka laboratory
October 23, 2018
Tweet
Share
More Decks by onizuka laboratory
See All by onizuka laboratory
Phrase-Based & Neural Unsupervised Machine Translation
onilab
0
120
Tell-and-Answer: Towards Explainable Visual Question Answering using Attributes and Captions
onilab
0
72
Card-660: A Reliable Evaluation Framework for Rare Word Representation Models
onilab
0
37
A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification
onilab
0
130
Integrating Transformer and Paraphrase Rules for Sentence Simplification
onilab
0
61
An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation
onilab
0
57
Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints
onilab
0
100
Modeling Multi-turn Conversation with Deep Utterance Aggregation
onilab
0
98
SGM: Sequence Generation Model for Multi-Label Classification
onilab
0
80
Other Decks in Research
See All in Research
世界モデルにおける分布外データ対応の方法論
koukyo1994
7
1.5k
生成的情報検索時代におけるAI利用と認知バイアス
trycycle
PRO
0
290
第二言語習得研究における 明示的・暗示的知識の再検討:この分類は何に役に立つか,何に役に立たないか
tam07pb915
0
1.1k
Multi-Agent Large Language Models for Code Intelligence: Opportunities, Challenges, and Research Directions
fatemeh_fard
0
120
[Devfest Incheon 2025] 모두를 위한 친절한 언어모델(LLM) 학습 가이드
beomi
2
1.4k
第66回コンピュータビジョン勉強会@関東 Epona: Autoregressive Diffusion World Model for Autonomous Driving
kentosasaki
0
350
2026年1月の生成AI領域の重要リリース&トピック解説
kajikent
0
320
大規模言語モデルにおけるData-Centric AIと合成データの活用 / Data-Centric AI and Synthetic Data in Large Language Models
tsurubee
1
490
Collective Predictive Coding and World Models in LLMs: A System 0/1/2/3 Perspective on Hierarchical Physical AI (IEEE SII 2026 Plenary Talk)
tanichu
1
250
Attaques quantiques sur Bitcoin : comment se protéger ?
rlifchitz
0
140
Self-Hosted WebAssembly Runtime for Runtime-Neutral Checkpoint/Restore in Edge–Cloud Continuum
chikuwait
0
330
学習型データ構造:機械学習を内包する新しいデータ構造の設計と解析
matsui_528
6
3.1k
Featured
See All Featured
HDC tutorial
michielstock
1
380
The B2B funnel & how to create a winning content strategy
katarinadahlin
PRO
1
280
How to Build an AI Search Optimization Roadmap - Criteria and Steps to Take #SEOIRL
aleyda
1
1.9k
技術選定の審美眼(2025年版) / Understanding the Spiral of Technologies 2025 edition
twada
PRO
117
110k
The Invisible Side of Design
smashingmag
302
51k
The Cost Of JavaScript in 2023
addyosmani
55
9.5k
Leo the Paperboy
mayatellez
4
1.4k
Git: the NoSQL Database
bkeepers
PRO
432
66k
Building a A Zero-Code AI SEO Workflow
portentint
PRO
0
310
Side Projects
sachag
455
43k
Sharpening the Axe: The Primacy of Toolmaking
bcantrill
46
2.7k
Context Engineering - Making Every Token Count
addyosmani
9
660
Transcript
$0-*/(:0.*,"* UI0DU -FBSOJOH4FNBOUJD4FOUFODF &NCFEEJOHT VTJOH1BJSXJTF %JTDSJNJOBUPS (3"%6"5&4$)00-PG*/'03."5*0/4$*&/$&BOE5&$)/0-0(: 04","6/*7 +6/:"5",":"."
1BQFS*/'0 #BESJ /BSBZBOB1BUSP 7JOPE,VNBS,VSNJ 4BOEFFQ,VNBS 7JOBZ/BNCPPEJSJ l-FBSOJOH4FNBOUJD4FOUFODF&NCFEEJOHT VTJOH 4FRVFOUJBM1BJSXJTF%JTDSJNJOBUPSz *O1SPDFFEJOHTPG$0-*/(
ݴ͍͑ੜϞσϧΛվྑͯͭ͠Α͍ &NCFEEJOH͕ಘΒΕΔΑ͏ʹͨ͠Α
$POUFOUT *OUSPEVDUJPO .FUIPET &YQFSJNFOUT $PODMVTJPO
#BDLHSPVOE • ҙຯ͕͍ۙจ͖ۙͮɼҙຯ͕ԕ͍จԕ͔͟ΔΑ͏ͳ จͷࢄදݱʢ&NCFEEJOHʣΛಘ͍ͨ • ݴ͍͑ੜ͕ղ͚ΔΑ͏ͳදݱΛ֫ಘ͢Εྑ͍ • 4FR4FRͰσίʔμͷηϧ͝ͱʹϩεΛܭࢉ͢ΔͨΊɼ จશମ͕ਖ਼͘͠ੜ͞ΕΔอূ͕ͳ͍ ˠࢀরจͱੜจͷҙຯతͳۙ͞Λอূ͢Δϩε͕ཉ͍͠
$POUSJCVUJPOT • ࢀরจͱੜจͷҙຯతͳۙ͞Λอূ͢Δ QBJSXJTFEJTDSJNJOBUPSMPTT HMPCBMMPTT ͷఏҊ • 4FOUJNFOU"OBMZTJTʹద༻ՄೳͰ͋Δ͜ͱΛࣔ͢ 4UBOGPSE4FOUJNFOU5SFFCBOLͰ 405"
$POUFOUT *OUSPEVDUJPO .FUIPET &YQFSJNFOUT $PODMVTJPO
.PEFM0WFSWJFX • ௨ৗͷ 4FR4FR ʹɼੜ݁Ռͱ (SPVOEUSVUIͷΤϯίʔυ݁Ռ͕ ۙ͘ͳΔΑ͏ʹֶश͢Δ 1BJSXJTF%JTDSJNJOBUPSΛՃ 1BJSXJTF%JTDSJNJOBUPS
&ODPEFS %FDPEFS -45. &ODPEFS (SPVOE5SVUI -PDBMMPTT DSPTTFOUSPQZ (MPCBMMPTT 4FOUFODF &NCFEEJOH
-PTTGVODUJPOT • -PDBMMPTT τʔΫϯ͝ͱͷ $SPTT&OUSPQZʢ௨ৗͷ 4FR4FRͷֶशͱಉ༷ʣ 1BJSXJTF%JTDSJNJOBUPS &ODPEFS
%FDPEFS -45. &ODPEFS (SPVOE5SVUI -PDBMMPTT $SPTT&OUSPQZ (MPCBMMPTT 4FOUFODF &NCFEEJOH
-PTTGVODUJPOT • (MPCBMMPTT ੜจͱࢀরจͷ &NCFEEJOHͷੵΛ࠷େԽ ੜจͱόονͷࢀরจҎ֎ͷจͷ &NCFEEJOHͷੵΛ࠷খԽ 1BJSXJTF%JTDSJNJOBUPS
&ODPEFS %FDPEFS -45. &ODPEFS (SPVOE5SVUI -PDBMMPTT DSPTTFOUSPQZ (MPCBMMPTT 4FOUFODF &NCFEEJOH
-PTTGVODUJPOT • ϩεؔʢ5PUBMʣ MPDBMMPTTͱ HMPCBMMPTTͷ 1BJSXJTF%JTDSJNJOBUPS &ODPEFS %FDPEFS
-45. &ODPEFS (SPVOE5SVUI -PDBMMPTT DSPTTFOUSPQZ (MPCBMMPTT 4FOUFODF &NCFEEJOH
$POUFOUT *OUSPEVDUJPO .FUIPET &YQFSJNFOUT $PODMVTJPO
&YQFSJNFOUTPWFSWJFX ͭͷλεΫͰ࣮ݧ • 1BSBQISBTFHFOFSBUJPO • 4FOUJNFOU"OBMZTJT
1BSBQISBTF(FOFSBUJPO • σʔληοτ 2VPSB EBUBTFU2VPSB ͷ࣭จ͔Βॏෳͨ͠ϖΞΛநग़ʢ,ʣ • ࣮ݧઃఆ ,Λ UFTUηοτɼ,Λ
WBMJEBUJPOηοτͱͯ͠༻͍Δ ੜจͱࢀরจͷྨࣅΛ #-&6ͰධՁ
1BSBQISBTF(FOFSBUJPO Ø#BTFMJOFʢ4FR4FRʣ ͱͷൺֱ %JTDSJNJOBUPSͱ &ODPEFSॏΈΛڞ༗ͨ͠ํ͕ྑ͍ σʔληοτͷαΠζʹؔΘΒͣɼ&%%-( TIBSFE ͕ͭΑ͍
1BSBQISBTF(FOFSBUJPO Øଞͷख๏ͱͷൺֱ
4FOUJNFOU"OBMZTJT • σʔληοτ 4UBOGPSE4FOUJNFOU5SFFCBOL 5SBJO, 7BMJE, 5FTU, • ࣮ݧઃఆ GJOFHSBJOFEDMBTTJGJDBUJPOUBTL
ྨ ͰධՁ \7FSZOFHBUJWF /FHBUJWF /FVUSBM 1PTJUJWF 7FSZQPTJUJWF^ ධՁࢦඪ &SSPS3BUF
4FOUJNFOU"OBMZTJT Ø݁Ռ
$POUFOUT *OUSPEVDUJPO .FUIPET &YQFSJNFOUT $PODMVTJPO
$PODMVTJPO • 4FR4FRϕʔεͷݴ͍͑ੜϞσϧʹΑΔจ &NCFEEJOHख๏ʹର͠ɼ ࢀরจͱੜจͷҙຯతͳۙ͞Λอূ͢Δ QBJSXJTFEJTDSJNJOBUPSMPTT HMPCBMMPTT ΛՃ • 1BSBQISBTF(FOFSBUJPO
4FOUJNFOU"OBMZTJTͰ 405"
͓ؾ࣋ͪʢ͋ͱͰফ͢εϥΠυʣ Ø 405"Λେʑతʹओு͢Δʹ͍Ζ͍Ζͱऑ͍ؾ͕͢Δʜ • 1BSBQISBTFଞͷσʔληοτࢼ͖͢Ͱʜ • 4FOUJNFOU 2VJDL5IPVHIUT ͱ͔ *OGFS4FOU
ͱൺֱ͖͢Ͱʜ ͳΜͰ GJOFHSBJOFE DMBTTFT ͚ͩͳͷ͔Θ͔Βͳ͍ʜ CJOBSZࡌͤͯ͘ΕͨΒ 25ͷจͱൺֱͰ͖Δͷʹʜ Ø %JTDSJNJOBUPS ػߏࣗମ 7"&ͷ ,-DPMMBQTF੍ͱ͔ʹ͑ͦ͏