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
データサイエンティストの業務変化
datascientistsociety
PRO
0
220
Earth AI: Unlocking Geospatial Insights with Foundation Models and Cross-Modal Reasoning
satai
3
480
Remote sensing × Multi-modal meta survey
satai
4
710
生成AI による論文執筆サポート・ワークショップ 論文執筆・推敲編 / Generative AI-Assisted Paper Writing Support Workshop: Drafting and Revision Edition
ks91
PRO
0
120
R&Dチームを起ち上げる
shibuiwilliam
1
160
令和最新技術で伝統掲示板を再構築: HonoX で作る型安全なスレッドフロート型掲示板 / かろっく@calloc134 - Hono Conference 2025
calloc134
0
550
財務諸表監査のための逐次検定
masakat0
1
250
20年前に50代だった人たちの今
hysmrk
0
140
When Learned Data Structures Meet Computer Vision
matsui_528
1
2.8k
LLM-jp-3 and beyond: Training Large Language Models
odashi
1
760
都市交通マスタープランとその後への期待@熊本商工会議所・熊本経済同友会
trafficbrain
0
120
An Open and Reproducible Deep Research Agent for Long-Form Question Answering
ikuyamada
0
270
Featured
See All Featured
Distributed Sagas: A Protocol for Coordinating Microservices
caitiem20
333
22k
Building Better People: How to give real-time feedback that sticks.
wjessup
370
20k
Being A Developer After 40
akosma
91
590k
Navigating Team Friction
lara
192
16k
Performance Is Good for Brains [We Love Speed 2024]
tammyeverts
12
1.4k
Thoughts on Productivity
jonyablonski
74
5k
Digital Projects Gone Horribly Wrong (And the UX Pros Who Still Save the Day) - Dean Schuster
uxyall
0
370
Bootstrapping a Software Product
garrettdimon
PRO
307
120k
Conquering PDFs: document understanding beyond plain text
inesmontani
PRO
4
2.3k
So, you think you're a good person
axbom
PRO
2
1.9k
How to Get Subject Matter Experts Bought In and Actively Contributing to SEO & PR Initiatives.
livdayseo
0
67
The B2B funnel & how to create a winning content strategy
katarinadahlin
PRO
1
280
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