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
文献紹介:Auxiliary Objectives for Neural Error Dete...
Search
Sponsored
·
SiteGround - Reliable hosting with speed, security, and support you can count on.
→
Atsushi
December 21, 2018
Technology
0
94
文献紹介:Auxiliary Objectives for Neural Error Detection Models
2018/12/21 文献紹介
長岡技術科学大学
自然言語処理研究室
Atsushi
December 21, 2018
Tweet
Share
More Decks by Atsushi
See All by Atsushi
文献紹介:Automated Evaluation of Out-of-Context Errors
atsumikan
0
99
文献紹介:Correction of OCR Word Segmentation Errors in Articles from the ACL Collection through Neural Machine Translation Methods
atsumikan
0
170
文献紹介:Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection
atsumikan
0
130
文献紹介:Low-resource OCR error detection and correction in French Clinical Texts
atsumikan
0
130
文献紹介:CMMC-BDRC Solution to the NLP-TEA-2018 Chinese Grammatical Error Diagnosis Task
atsumikan
0
130
文献紹介 : Fluency Boost Learning and Inference for Neural Grammatical Error Correction
atsumikan
0
190
文献紹介:語彙の概念化と Wikipediaを用いた英字略語の意味推定方法
atsumikan
0
160
文献紹介:The Effect of Error Rate in Artificially Generated Data for Automatic Preposition and Determiner Correction
atsumikan
0
140
文献紹介: Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction
atsumikan
0
180
Other Decks in Technology
See All in Technology
管理者向けGitHub Enterpriseの運用Tips紹介: 人にもAIにも優しいプラットフォームづくり
yuriemori
0
110
オンプレとGoogle Cloudを安全に繋ぐための、セキュア通信の勘所
waiwai2111
3
1.1k
Exadata Fleet Update
oracle4engineer
PRO
0
1.3k
男(監査)はつらいよ - Policy as CodeからAIエージェントへ
ken5scal
5
730
Lookerの最新バージョンv26.2がやばい話
waiwai2111
1
150
Oracle Base Database Service 技術詳細
oracle4engineer
PRO
15
95k
社内でAWS BuilderCards体験会を立ち上げ、得られた気づき / 20260225 Masaki Okuda
shift_evolve
PRO
1
160
Claude Codeの進化と各機能の活かし方
oikon48
7
2k
EMからVPoEを経てCTOへ:マネジメントキャリアパスにおける葛藤と成長
kakehashi
PRO
6
850
作るべきものと向き合う - ecspresso 8年間の開発史から学ぶ技術選定 / 技術選定con findy 2026
fujiwara3
7
2.1k
Agentic Codingの実践とチームで導入するための工夫
lycorptech_jp
PRO
0
400
Oracle Database@Google Cloud:サービス概要のご紹介
oracle4engineer
PRO
5
1.1k
Featured
See All Featured
Automating Front-end Workflow
addyosmani
1370
200k
Gemini Prompt Engineering: Practical Techniques for Tangible AI Outcomes
mfonobong
2
300
Reality Check: Gamification 10 Years Later
codingconduct
0
2k
Marketing Yourself as an Engineer | Alaka | Gurzu
gurzu
0
140
Why You Should Never Use an ORM
jnunemaker
PRO
61
9.8k
What’s in a name? Adding method to the madness
productmarketing
PRO
24
4k
A Guide to Academic Writing Using Generative AI - A Workshop
ks91
PRO
0
230
<Decoding/> the Language of Devs - We Love SEO 2024
nikkihalliwell
1
150
Practical Tips for Bootstrapping Information Extraction Pipelines
honnibal
25
1.8k
コードの90%をAIが書く世界で何が待っているのか / What awaits us in a world where 90% of the code is written by AI
rkaga
60
42k
Primal Persuasion: How to Engage the Brain for Learning That Lasts
tmiket
0
280
Code Review Best Practice
trishagee
74
20k
Transcript
Ԭٕज़Պֶେֶࣗવݴޠॲཧݚڀࣨ ੁ३ࢤ จݙհ ݄ .BSFL3FJ )FMFO:BOOBLPVEBLJT 1SPDFFEJOHTPGUIFUI8PSLTIPQPO*OOPWBUJWF6TFPG/-1GPS #VJMEJOH&EVDBUJPOBM"QQMJDBUJPOT QBHFTr$PQFOIBHFO
%FONBSL 4FQUFNCFS "VYJMJBSZ0CKFDUJWFT GPS/FVSBM&SSPS%FUFDUJPO.PEFMT
"CTUSBDU w ݴޠֶशऀͷ࡞ͨ͠จͷޡΓݕग़ w χϡʔϥϧΛ༻͍ͨܥྻϥϕϦϯάख๏ʹ͓͍ͯɺ ิॿతͷ܇࿅ͷ༗༻ੑΛݕূ w ҎલͷޡΓݕγεςϜͱಉ͡ͷύϥϝʔλͰ ΑΓྑ͍ύϑΥʔϚϯεΛୡ !2
*OUSPEVDUJPO w ݴޠֶशऀͷ࡞ͨ͠จͰ༷ʑͳλΠϓͷޡΓΛ ࣝผ͢Δඞཁ͕͋Δ ɾػೳޠͷޡͬͨ༻ ɾ༰ޠͷҙຯతͳޡΓʢલஔࢺɾܗ༰ࢺͷΈ߹ΘͤͳͲʣ w ݴޠͷΑΓྑ͍දݱΛֶͼɺจ຺ʹ͓͚ΔޡΓΛ ΑΓਖ਼֬ʹݕग़Ͱ͖ΔγεςϜΛߏங͢Δ w
ਖ਼൱ͷ༧ଌ͚ͩͰͳ͘طଘͷσʔλ͔Βநग़Ͱ͖Δ ใΛ༧ଌ͢Δ͜ͱΛࢼΈΔ !3
"VYJMJBSZ-PTT'VODUJPOT !4 xt h( f ) t h(b) t dt
yt DPSSFDUJODPSSFDU 8PSE yt,k : MBCFMLΛ࣋ͭτʔΫϯUͷ༧ଌ֬ ˜ yt,k : ̍τʔΫϯUͷਖ਼͍͠ϥϕϧ͕Lͷ࣌ ̌ͦΕҎ֎ͷ߹
"VYJMJBSZ-PTT'VODUJPOT !5 xt y(1) t y(2) t d(1) t d(2)
t h( f ) t h(b) t DPSSFDUJODPSSFDU 8PSE ผͷλεΫ yt,k : MBCFMLΛ࣋ͭτʔΫϯUͷ༧ଌ֬ ˜ yt,k : ̍τʔΫϯUͷਖ਼͍͠ϥϕϧ͕Lͷ࣌ ̌ͦΕҎ֎ͷ߹
"VYJMJBSZ-PTT'VODUJPOT w GSFRVFODZ ୯ޠͷසΛ༧ଌ͢Δ w FSSPSUZQF ୯ޠͷޡΓͷछྨΛ༧ଌ͢Δ w pSTUMBOHVBHF ֶशऀͷୈҰݴޠΛ༧ଌ͢Δ
w QBSUPGTQFFDI ୯ޠͷࢺΛ༧ଌ͢Δ w HSBNNBUJDBMSFMBUJPOT ୯ޠؒͷґଘؔΛ༧ଌ͢Δ ɹ !6 ผͷλεΫ
"VYJMJBSZ-PTT'VODUJPOT !7
&WBMVBUJPOTFUVQBOEEBUBTFUT w #BTFMJOFɿ3FJBOE:BOOBLPVEBLJT ɾ#J-45.Λ༻͍ͯ4P5"Λୡͨ͠Ϟσϧ w %BUBTFUT ɾ'JSTU$FSUJpDBUFJO&OHMJTI '$&
EBUBTFU ɾ$P/--TIBSFEUBTLUFTUTFU !8
&WBMVBUJPOTFUVQBOEEBUBTFUT w ύϥϝʔλ ɾXPSEFNCFEEJOHTTJ[F ɾJOJUJBMJ[FE QVCMJDMZBWBJMBCMFXPSEWFD .JLPMPWFUBM ɹFNCFEEJOHTUSBJOFEPO(PPHMF/FXT
ɾ-45.IJEEFOMBZFST ɾUBTLTQFDJpDIJEEFOMBZFST ɾPQUJNJ[FE"EBEFMUB ;FJMFS !9
!10
"MUFSOBUJWF5SBJOJOH4USBUFHJFT w ϚϧνλεΫֶशʹؔ͢Δݚڀ ෳͷσʔληοτͰಉ͡γεςϜΛ࠷దԽ͢Δ͜ͱʹ ॏΛஔ͍͍ͯΔ w ༗༻ੑΛࣔͨ͢Ίɺ࣍ͷσʔληοτΛ༻͍ͯ܇࿅ ɾ$P/--EBUBTFUʢDIVOLJOHʣ ɹɹ 5KPOH,JN4BOHBOE#VDIIPM[
ɾ$P/--DPSQVTʢ/&3ʣ ɹɹ 5KPOH,JN4BOHBOE%F.FVMEFS ɾ1FOO5SFFCBOL 15# 104DPSQVT ɹɹ .BSDVTFUBM !11
"MUFSOBUJWF5SBJOJOH4USBUFHJFT w ࣍ͷͭͷํ๏Ͱ܇࿅͢Δ ผͷλεΫͷσʔληοτͰ܇࿅ͨ͋͠ͱɺ ޡΓݕग़σʔληοτͰ܇࿅ ผͷλεΫͷσʔληοτͱޡΓݕग़ͷ σʔληοτΛަޓʹ܇࿅ !12
"MUFSOBUJWF5SBJOJOH4USBUFHJFT !13
"MUFSOBUJWF5SBJOJOH4USBUFHJFT !14
"EEJUJPOBM5SBJOJOH%BUB w NVMUJUBTLMFBSOJOH ར༻ՄೳͳλεΫݻ༗ͷ܇࿅σʔλ͕গͳ͍࣌ʹޮՌ͕ ظ͞ΕΔ w େنͳσʔληοτΛ༻͍ͨ߹ͷޮՌΛݕূ !15
"EEJUJPOBM5SBJOJOH%BUB w ࣍ͷσʔληοτΛ༻ʢ߹ܭ.UPLFOTʣ ɾ$BNCSJEHF-FBSOFS$PSQVT $-$ /JDIPMMT ɾ/64$PSQVTPG-FBSOFS&OHMJTI /6$-& %BIMNFJFSFUBM
ɾ-BOHDPSQVT .J[VNPUPFUBM w܇࿅࣌ʹֶश͢ΔλεΫ ɾ&SSPS%FUFDU ɾ104UBHHJOH !16
"EEJUJPOBM5SBJOJOH%BUB !17
$PODMVTJPO w ݴޠֶशऀͷ࡞ͨ͠จͷޡΓݕΛվળ͢ΔͨΊʹ χϡʔϥϧܥྻϥϕϦϯάʹิॿతͳଛࣦؔΛ౷߹ w 104λάɺจ๏తؔɺޡΓͷछྨ͕ޡΓݕग़ʹ༗༻Ͱ Έ߹ΘͤΔ͜ͱͰ݁Ռ͕վળ w ར༻Մೳͳ܇࿅σʔλ͕ݶΒΕ͍ͯΔ͚࣌ͩͰͳ͘ ଟ͘ͷ܇࿅σʔλΛ༻ͨ͠߹Ͱ༗ޮ
!18