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
Generating More Interesting Responses in Neural...
Search
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
onizuka laboratory
December 18, 2018
Research
110
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints
弊研究室で行なったEMNLP2018読み会の発表資料です。
onizuka laboratory
December 18, 2018
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
82
Card-660: A Reliable Evaluation Framework for Rare Word Representation Models
onilab
0
43
A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification
onilab
0
140
Integrating Transformer and Paraphrase Rules for Sentence Simplification
onilab
0
66
An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation
onilab
0
62
Modeling Multi-turn Conversation with Deep Utterance Aggregation
onilab
0
100
Learning Semantic Sentence Embeddings using Pair-wise Discriminator
onilab
0
130
SGM: Sequence Generation Model for Multi-Label Classification
onilab
0
87
Other Decks in Research
See All in Research
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
1k
FUSE-RSVLM: Feature Fusion Vision-Language Model for Remote Sensing
satai
3
840
「AIとWhyを深堀る」をAIと深堀る
iflection
0
470
セマンティック通信勉強会 6Gに向けたデバイス間効率的な通信の技術紹介・課題・今後展望
satai
3
150
さくらインターネット研究所テックトーク2026春、研究開発Gr.25年度成果26年度方針
kikuzo
0
140
Apache Gravitinoで実現する Icebergカタログ統合とアクセスの一元化
matsumooon
0
260
計算情報学研究室(数理情報学第7研究室)2026
tomohirokoana
0
520
SOTAのさらに先へ:厳しい推論制約下での高性能モデルのPost-Training
analokmaus
0
1.2k
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
shunk031
4
1k
言語モデルから言語について語る際に押さえておきたいこと
eumesy
PRO
5
2.3k
typst の使い方:言語学を研究する学生のために
gitomochang
0
450
英語教育 “研究” のあり方:学術知とアウトリーチの緊張関係
terasawat
1
990
Featured
See All Featured
Avoiding the “Bad Training, Faster” Trap in the Age of AI
tmiket
0
170
First, design no harm
axbom
PRO
2
1.2k
KATA
mclloyd
PRO
35
15k
The Psychology of Web Performance [Beyond Tellerrand 2023]
tammyeverts
49
3.5k
Ecommerce SEO: The Keys for Success Now & Beyond - #SERPConf2024
aleyda
1
2k
Docker and Python
trallard
47
3.9k
How to Ace a Technical Interview
jacobian
281
24k
DBのスキルで生き残る技術 - AI時代におけるテーブル設計の勘所
soudai
PRO
65
55k
10 Git Anti Patterns You Should be Aware of
lemiorhan
PRO
659
62k
Visual Storytelling: How to be a Superhuman Communicator
reverentgeek
2
550
We Are The Robots
honzajavorek
0
240
Abbi's Birthday
coloredviolet
2
7.9k
Transcript
&./-1:0.*,"* UI%FD (FOFSBUJOH .PSF*OUFSFTUJOH3FTQPOTFT JO/FVSBM$POWFSTBUJPO.PEFMT XJUI %JTUSJCVUJPOBM$POTUSBJOUT (3"%6"5&4$)00-PG*/'03."5*0/4$*&/$&BOE5&$)/0-0(: 04","6/*7
+6/:"5",":"."
1BQFS*/'0 "TIVUPTI #BIFUJ "MBO3JUUFS +JXFJ -J BOE#JMM%PMBO l(FOFSBUJOH.PSF*OUFSFTUJOH3FTQPOTFTJO/FVSBM $POWFSTBUJPO.PEFMTXJUI%JTUSJCVUJPOBM$POTUSBJOUTz *O1SPDFFEJOHTPG&./-1
ೖྗൃͱͷτϐοΫҰ؏ੑҙຯతྨࣅ͕ߴ͍ԠจΛੜ͢ΔΑ͏ʹ ੍Λ͔͚ͯσίʔυ͢Δ͜ͱͰɼ%VMM3FTQPOTFʹରॲͨ͠Α
$POUFOUT *OUSPEVDUJPO .FUIPET &YQFSJNFOUT $PODMVTJPO
*TTVF Ø࠷ਪఆʹΑΔ UFYUUPUFYUੜϞσϧೖྗจͱग़ྗจ͕ ΄΅ҰରҰରԠͷλεΫʢػց༁ͳͲʣͰ༗༻ ØԠੜଟରଟͷͨΊ࠷ਪఆͰʮͦ͏Ͱ͢Ͷʯʮ͍ʯ ͳͲͷ EVMMSFTQPOTFΛੜ͕ͪ͠
*TTVF ØަࠩΤϯτϩϐʔଛࣦ ʹΑΔԠੜ ʹ͓͚Δσίʔυྫ • τϐοΫϫʔυʹൺɼ ετοϓϫʔυͷ͕ ߴ͘ͳΓ͕ͪ • සͷ͍ޠ
ੜͮ͠Β͍
0WFSWJFX (PBMೖྗൃͱରԠͨ͠༰ޠΛଟؚ͘ΉΑ͏ͳԠจͷੜ 1SPQPTBM ೖྗจ 9 ग़ྗจ : ͱͨ͠ͱ͖ɼ • ग़ྗจͱԠจͷτϐοΫͷҰ؏ੑ
!"# $ % & , $ % ( • ग़ྗจͱԠจͷҙຯతྨࣅ !"# )#* & , )#* ( Λߟྀͨ͠ԠΛੜ͢ΔΑ͏ʹॏΈ͚͠ͳ͕Βσίʔυ͢Δ
$POUFOUT *OUSPEVDUJPO .FUIPET &YQFSJNFOUT $PODMVTJPO
"QQSPBDI ØτϐοΫͷҰ؏ੑ͕ߴ͘ͳΔΑ͏ʹग़ྗʢτϐοΫ੍ʣ Øग़ྗͱԠͷྨࣅ͕ߴ͘ͳΔΑ͏ʹग़ྗʢҙຯ੍ʣ τϐοΫྨࣅ ҙຯతྨࣅ
࣮ࡍͷσίʔυ୯ޠ୯ҐͰஞ࣍తʹग़ྗΛߦ͏ͨΊɼ τϐοΫ੍ɾҙຯ੍ʹ༻͍ΔείΞҎԼͷͭͷ݅ʀ • σίʔυ్தͷෆશͳจΛར༻Մೳ • ܭࢉίετ͕͍ Λຬͨ͢ඞཁ͕͋Δ %FDPEJOHXJUI%JTUSJCVUJPOBM $POTUSBJOUT*O
! " # ͷਪఆʹ )..-%"ϞσϧΛ࠾༻ จதͷ֤୯ޠʹ͍ͭͯɼͦͷ୯ޠ͕ • τϐοΫ 5Ͱ͋Δ֬ •
τϐοΫϫʔυͰ͋Δ֬ ͷੵΛͱΓɼτϐοΫϫʔυͷʢظʣͰॏΈ͚ฏۉ τϐοΫྨࣅ
! " # ͷਪఆʹ )..-%"ϞσϧΛ࠾༻ ཁ͢Δʹɿ୯ޠ͝ͱͷτϐοΫਪఆ݁ՌΛ͠߹Θͤ ˠσίʔυதͷෆશͳจʹஞ࣍తʹద༻Մೳ ྨࣅؔ ∆ !("|'
, ! ) ' ʹ୯७ͳυοτੵΛ࠾༻ τϐοΫྨࣅ
จͷࢄදݱ !"# $ Λ "SPSBΒ <>ͷख๏Λ༻͍ͯಘΔɿ ୯ޠͷϢχάϥϜ֬ % &
ύϥϝʔλ ' ୯ޠࢄදݱ () Λ༻͍ͯɼ(* = ∑ )∈* . ./0 ) () Λܭࢉ (* ͔ΒୈҰओ ཁૉΛҾ͘ ୈҰओػೳޠͷӨڹ͕େ͖͍ʢΒ͍͠ʣ ࣮ࡍʹ܇࿅ίʔύεΛ༻͍ͯओੳΛࣄલʹߦ͍ɼ σίʔυ࣌ʹͦͷ݁ՌΛར༻ʢίʔυΛݟΔݶΓʣ ҙຯతྨࣅ .0 7 27,- 2 .1 2 1 1 0 .1, A .
จͷࢄදݱ !"# $ Λ "SPSBΒ <>ͷख๏Λ༻͍ͯಘΔɿ ཁ͢Δʹɿ୯ޠ͝ͱͷࢄදݱͷॏΈ͖ฏۉ ˠσίʔυ࣌ʹஞ࣍తʹૉૣ͘ܭࢉՄೳ ྨࣅؔ %
&' , &) ʹ୯७ͳυοτੵΛ࠾༻ ҙຯతྨࣅ .0 7 27,- 2 .1 2 1 1 0 .1, A .
$POUFOUT *OUSPEVDUJPO .FUIPET &YQFSJNFOUT $PODMVTJPO
4FUUJOHT • σʔληοτ ܇࿅σʔλɿ0QFO4VCUJUMFTʢ.VSQBJSTʣ ςετσʔλɿ$PSOFMM.PWJF%JBMPHVF$PSQVTʢQBJST ʣ • ϕʔεϥΠϯ ..*Ԡͱೖྗͷ૬ޓใྔΛ࠷େԽ͢ΔϞσϧ !
= argmax( log + ! , + - 5"4FR4FRτϐοΫਪఆ݁ՌΛ "UUFOUJPOܭࢉʹ༻͍ΔϞσϧ
3FTVMUT "VUPNBUJD&WBMVBUJPO #-&6Λେ͖͘Լ͛ͣʹԠͷଟ༷ੑΛ্ ετοϓϫʔυ੍ʹޮՌ͋Γ
3FTVMUT )VNBO ਓखධՁͰԠͷΒ͠͞ΛଛͶͣʹ༰ͷॆ্࣮͕
$POUFOUT *OUSPEVDUJPO .FUIPET &YQFSJNFOUT $PODMVTJPO
$PODMVTJPO Ø ࢄදݱͷܗͰग़ྗͱԠͷτϐοΫྨࣅͱҙຯతྨࣅΛ ࢉग़͠ɼσίʔυ࣌ͷ੍ͱͯ͠ΈࠐΜͩ Ø ԠͷΒ͠͞ΛԼ͛Δ͜ͱͳ͘ɼଟ༷ੑΛ্ͤͨ͞