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
vampire.pdf
Search
MARUYAMA
February 25, 2020
200
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
vampire.pdf
MARUYAMA
February 25, 2020
More Decks by MARUYAMA
See All by MARUYAMA
Misspelling_Oblivious_Word_Embedding.pdf
tmaru0204
0
210
Simple_Unsupervised_Summarization_by_Contextual_Matching.pdf
tmaru0204
0
200
Controlling_Text_Complexity_in_Neural_Machine_Translation.pdf
tmaru0204
0
180
20191028_literature-review.pdf
tmaru0204
0
160
Hint-Based_Training_for_Non-Autoregressive_Machine_Translation.pdf
tmaru0204
0
150
Soft_Contextual_Data_Augmentation_for_Neural_Machine_Translation_.pdf
tmaru0204
0
180
An_Embarrassingly_Simple_Approach_for_Transfer_Learning_from_Pretrained_Language_Models_.pdf
tmaru0204
0
170
Addressing_Trobulesome_Words_in_Neural_Machine_Translation.pdf
tmaru0204
0
180
Simple_Unsupervised_Keyphrase_Extraction_using_Sentence_Embeddings.pdf
tmaru0204
0
200
Featured
See All Featured
SEO Brein meetup: CTRL+C is not how to scale international SEO
lindahogenes
1
2.8k
Build your cross-platform service in a week with App Engine
jlugia
234
18k
Paper Plane
katiecoart
PRO
2
52k
Getting science done with accelerated Python computing platforms
jacobtomlinson
2
360
How to train your dragon (web standard)
notwaldorf
97
6.7k
Git: the NoSQL Database
bkeepers
PRO
432
67k
Practical Tips for Bootstrapping Information Extraction Pipelines
honnibal
25
2k
Performance Is Good for Brains [We Love Speed 2024]
tammyeverts
12
1.8k
The Invisible Side of Design
smashingmag
301
52k
The SEO identity crisis: Don't let AI make you average
varn
0
520
A brief & incomplete history of UX Design for the World Wide Web: 1989–2019
jct
2
430
Jess Joyce - The Pitfalls of Following Frameworks
techseoconnect
PRO
1
310
Transcript
Variational Pretraining for Semi-supervised Text Classification จݙհ Suchin Gururangan, Tam
Dang, Dallas Card, Noah A. Smith ACL2019, pages 5880–5894
Abstract ⾣ܰྔͳࣄલֶशख๏ΛఏҊ ɾখنσʔλͰޮతʹֶशՄೳ ɾߴʹಈ࡞ ⾣ςΩετྨλεΫʹ͓͍ͯɺ ɹ&-.P #&35ʹඖఢ͢ΔੑೳΛୡ
Introduction ⾣&-.P #&35ͷࣄલֶशϞσϧ͕ ɹ͘ར༻͞Ε͍ͯΔ ⾣େنͳίʔύεɾܭࢉࢿݯ͕ඞཁ ⾣ܰྔ͔ͭޮՌతͳࣄલֶशํ๏ΛఏҊ
Model ⾣1SFUSBJOJOH ɾ7BSJBUJPOBM"VUP&ODPEFS
Model ⾣1SFUSBJOJOH ɾ7BSJBUJPOBM"VUP&ODPEFS
Model ⾣5FYUDMBTTJpDBUJPO
Model ⾣5FYUDMBTTJpDBUJPO ɾ7".1*3&FNCFEEJOH݁߹ ɾ%FFQ"WFSBHF/FUXPSLͰ Τϯίʔυ
Experimental setup ⾣%BUB ɾࣄલֶशσʔλ ɾྨثֶशςετ d FYBNQMFT JOEPNBJO d
FYBNQMFT
Experimental setup ⾣-PXSFTPVSDFTFUUJOH ɾ#BTFMJOFڭࢣσʔλͷΈͰֶश ɾ4FMGUSBJOJOHڭࢣ͋Γֶश ڭࢣσʔλ Ϟσϧͷ༧ଌ݁Ռ ɾ(-07& *%
JOEPNBJOσʔλͰֶश ɾ(-07& 0% .XPSETͷίʔύεͰֶश
Results ⾣-PXSFTPVSDFTFUUJOH
Results ⾣-PXSFTPVSDFTFUUJOH
Experimental setup ⾣)JHISFTPVSDFTFUUJOH ɾ5SBOTGPSNFSCBTFE&-.P ɾ#&35 GSP[FO pOFUVOJOH QSFUSBJOJOH JOEPNBJO GSP[FO
pOFUVOJOH
Results ⾣)JHISFTPVSDFTFUUJOH
Results ⾣)JHISFTPVSDFTFUUJOH
Results ⾣$PNQVUBUJPOBM3FRVJSFNFOUT
Conclusions ⾣ܰྔͳࣄલֶशख๏ΛఏҊ ɾখنσʔλͰޮతʹֶशՄೳ ɾߴʹಈ࡞ ⾣ςΩετྨλεΫʹ͓͍ͯɺ ɹ&-.P #&35ʹඖఢ͢ΔੑೳΛୡ