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
Dynamic_Data_Selection_for_Neural_Machine_Trans...
Search
MARUYAMA
March 28, 2019
0
130
Dynamic_Data_Selection_for_Neural_Machine_Translation.pdf
MARUYAMA
March 28, 2019
Tweet
Share
More Decks by MARUYAMA
See All by MARUYAMA
vampire.pdf
tmaru0204
0
190
Misspelling_Oblivious_Word_Embedding.pdf
tmaru0204
0
210
Simple_Unsupervised_Summarization_by_Contextual_Matching.pdf
tmaru0204
0
190
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
160
Addressing_Trobulesome_Words_in_Neural_Machine_Translation.pdf
tmaru0204
0
180
Featured
See All Featured
VelocityConf: Rendering Performance Case Studies
addyosmani
333
24k
JavaScript: Past, Present, and Future - NDC Porto 2020
reverentgeek
52
5.9k
Code Reviewing Like a Champion
maltzj
528
40k
Connecting the Dots Between Site Speed, User Experience & Your Business [WebExpo 2025]
tammyeverts
11
850
Responsive Adventures: Dirty Tricks From The Dark Corners of Front-End
smashingmag
254
22k
Exploring the Power of Turbo Streams & Action Cable | RailsConf2023
kevinliebholz
37
6.3k
Statistics for Hackers
jakevdp
799
230k
職位にかかわらず全員がリーダーシップを発揮するチーム作り / Building a team where everyone can demonstrate leadership regardless of position
madoxten
59
50k
CoffeeScript is Beautiful & I Never Want to Write Plain JavaScript Again
sstephenson
162
16k
Money Talks: Using Revenue to Get Sh*t Done
nikkihalliwell
0
170
Balancing Empowerment & Direction
lara
5
930
Stop Working from a Prison Cell
hatefulcrawdad
274
21k
Transcript
%ZOBNJD%BUB4FMFDUJPO GPS/FVSBM.BDIJOF5SBOTMBUJPO .BSMJFTWBOEFS8FFT "SJBOOB#JTB[[B $ISJTUPG.PO[&/.-1 QBHFTr -JUFSBUVSFSFWJFX /BHBPLB6OJWFSTJUZPG5FDIOPMPHZ5BLVNJ.BSVZBNB
"CTUSBDU ⾣ֶश࣌ؒΛݮͭͭ͠ɺੑೳΛվળ ⾣/.5ͷֶशதʹɺಈతʹֶशσʔλΛมԽͤ͞Δख๏ΛఏҊ ⾣طଘͷσʔλબख๏Λ/.5ʹదԠ͠ɺޮՌΛݕূ ˠ1#.5ʹޮՌత͕ͩɺ/.5ʹ͓͍ͯ͋·ΓޮՌ͕ͳ͍
*OUSPEVDUJPO ⾣ͭͷ࣮ݧ ɾطଘͷσʔλબख๏ 4UBUJD%BUB4FMFDUJPO ɾ/.5ʹ߹Θͤͨσʔλબख๏ %ZOBNJD%BUB4FMFDUJPO 4BNQMJOH (SBEVBMpOFUVOJOH
4UBUJD%BUB4FMFDUJPO ɾ(FOFSBM ɾ*OEPNBJO ςετσʔλͱಉ͡υϝΠϯͷσʔλ ⾣छྨͷݴޠϞσϧΛߏங *OEPNBJO (FOFSBM 4PVSDFTJEF 5BSHFUTJEF º
⾣ͭͷίʔύεΛར༻
4UBUJD%BUB4FMFDUJPO $ίʔύε I*OEPNBJO G(FOFSBM Cݴޠ fTPVSDFTJEF eUBSHFUTJEF ⾣֤ݴޠϞσϧΛ༻͍ͯɺDSPTTFOUSPQZEJ⒎FSFODF
$&% Λܭࢉ શֶशσʔλΛ$&%ॱʹιʔτͯ͠ɺ্ҐOจରΛֶशʹར༻
%ZOBNJD%BUB4FMFDUJPO ⾣4BNQMJOH $&%ʹج͍ͮͨॏΈ͖αϯϓϦϯά ςετσʔλͱؔ࿈ͷ͋ΔσʔλબΕ͍͢
%ZOBNJD%BUB4FMFDUJPO ⾣(SBEVBMpOFUVOJOH FQPDIΛॏͶΔʹɺؔ࿈ͷ͍σʔλΛֶशσʔλ͔Βআ֎
%ZOBNJD%BUB4FMFDUJPO ⾣(SBEVBMpOFUVOJOH iFQPDIͷσʔλαΠζn(i) ЋSFMBUJWFTUBSUTJ[F 㱡Ћ㱡 cGcUSBJOJOHEBUBTJ[F ЌSFUFOUJPOSBUF 㱡Ќ㱡 Б/VNCFSPGDPOTFDVUJWFFQPDI TFMFDUFETVCTFUJTVTFE
Б㱢
&YQFSJNFOUTFUUJOH ⾣(FSNBOˠ&OHMJTI ⾣.BDIJOF5SBOTMBUJPO4ZTUFNT ɾ/.5&ODPEFSEFDPEFSNPEFMXJUIHMPCBMBUUFOUJPO ɾ1#.5.PTFT
&YQFSJNFOUTFUUJOH ⾣%BUBTFU ɾ&.&"NFEJDBMHVJEFMJOFT ɾ.PWJFEJBMPHVFT ɾ5&%UBMLT ɾ8.5OFXT ֶश։ൃσʔλʹ.JY ධՁʹ֤ධՁηοτΛར༻
3FTVMU ⾣4UBUJDEBUBTFMFDUJPOGPS1#.5BOE/.5 JOEPNBJOͷσʔλͷΈ ˒ 4UBUJDEBUBTFMFDUJPO ˔ JOEPNBJOͷσʔλͷΈ ˒
4UBUJDEBUBTFMFDUJPO ˔ 1#.5 /.5
3FTVMU ⾣%ZOBNJDEBUBTFMFDUJPOGPS/.5 ಉֶ͡श࣌ؒʹ͓͍ͯɺ TUBUJDTFMFDUJPOΑΓྑ͍݁Ռ 8.5OFXTΛআ͘σʔλͰɺ TUBUJDTFMFDUJPOͷ࠷ऴ݁ՌΑΓ ߴ͍είΞ
3FTVMU ⾣%ZOBNJDEBUBTFMFDUJPOGPS/.5
$PODMVTJPO ⾣ֶश࣌ؒΛݮͭͭ͠ɺੑೳΛվળ ⾣/.5ͷֶशதʹɺಈతʹֶशσʔλΛมԽͤ͞Δख๏ΛఏҊ ⾣طଘͷσʔλબख๏Λ/.5ʹదԠ͠ɺޮՌΛݕূ ˠ1#.5ʹޮՌత͕ͩɺ/.5ʹ͓͍ͯ͋·ΓޮՌ͕ͳ͍