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: Similarity-Based Reconstruction Loss for ...
Search
Yumeto Inaoka
May 26, 2019
Research
250
1
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
文献紹介: Similarity-Based Reconstruction Loss for Meaning Representation
2019/05/28の文献紹介で発表
Yumeto Inaoka
May 26, 2019
More Decks by Yumeto Inaoka
See All by Yumeto Inaoka
文献紹介: Quantity doesn’t buy quality syntax with neural language models
yumeto
1
220
文献紹介: Open Domain Web Keyphrase Extraction Beyond Language Modeling
yumeto
0
290
文献紹介: Self-Supervised_Neural_Machine_Translation
yumeto
0
200
文献紹介: Comparing and Developing Tools to Measure the Readability of Domain-Specific Texts
yumeto
0
210
文献紹介: PAWS: Paraphrase Adversaries from Word Scrambling
yumeto
0
220
文献紹介: Beyond BLEU: Training Neural Machine Translation with Semantic Similarity
yumeto
0
330
文献紹介: EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing
yumeto
0
440
文献紹介: Decomposable Neural Paraphrase Generation
yumeto
0
260
文献紹介: Analyzing the Limitations of Cross-lingual Word Embedding Mappings
yumeto
0
300
Other Decks in Research
See All in Research
Karkada さんの論文 × 2 の紹介: (1) Closed-Form Training Dynamics Reveal Learned Features and Linear Structure in Word2Vec-like Models, (2) Symmetry in language statistics shapes the geometry of model representations
eumesy
PRO
1
700
Claude Code × autoresearch 実践
mathbullet
0
270
【中間報告】国会議員の立法・政策実務を支える環境を巡る現状と課題
polipoli
0
570
Research Engineerという仕事 / Research Engineering: Bridging Research and Business
chck
1
300
[最先端NLP勉強会2026] Checklists Are Better Than Reward Models For Aligning Language Models
nzw0301
1
310
[最先端NLP勉強会2026] Agentic Rubrics as Contextual Verifiers for SWE Agents
rfujii
1
340
Google Cloud Next 2026 DM Recap Agentic Data Cloudを添えて / Google Cloud Next 2026 DM Recap
nnaka2992
0
130
Model Discovery and Graph Simulation: A Lightweight Gateway to Chaos Engineering
anatolykr
0
300
NLP colloquium: AI Safety Survey
kanekomasahiro
1
1.1k
SAM3を用いたコマ・吹き出しの 領域検出と分割構造からの読み順推定
kzmssk
0
110
LLM の Attention 機構まとめ — 数式・計算量・メモリ
puwaer
8
2.6k
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
500
Featured
See All Featured
Jamie Indigo - Trashchat’s Guide to Black Boxes: Technical SEO Tactics for LLMs
techseoconnect
PRO
0
660
sira's awesome portfolio website redesign presentation
elsirapls
0
410
Why You Should Never Use an ORM
jnunemaker
PRO
61
10k
Documentation Writing (for coders)
carmenintech
77
5.5k
Facilitating Awesome Meetings
lara
57
7.1k
Google's AI Overviews - The New Search
badams
0
1.6k
Amusing Abliteration
ianozsvald
1
290
Effective software design: The role of men in debugging patriarchy in IT @ Voxxed Days AMS
baasie
0
510
Thoughts on Productivity
jonyablonski
76
5.4k
We Have a Design System, Now What?
morganepeng
55
8.3k
How STYLIGHT went responsive
nonsquared
100
6.3k
Stop Working from a Prison Cell
hatefulcrawdad
274
21k
Transcript
Similarity-Based Reconstruction Loss for Meaning Representation
Literature 2
Abstract • • • 3
Introduction • • 4
Related Work • • • • 5
Related Work • • 6
Auto-Encoder •ℒ , • • • • 7
Weighted similarity loss •ℒ = − σ =1 sim ,
• • • : • • sim() • 8
Weighted cross-entropy loss •ℒ = − σ =1 sim ,
log( ) • • 9
Soft label loss •ℒ = − σ =1 ∗log •
∗ = ൞ sim , σ =1 sim(,) , ∈ top N 0 , ∉ top N • • 10
True-label encoding 11
Tasks & Datasets • • • 12
Results 13
Results 14
Additional Experiments • • 15
Results • • 16
Results 17
Results 18
Results 19
Discussion • • 20
Conclusion • • • • 21