Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Sign up for free
Menu
Search
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Pricing
Search
Sign in
Sign up for free
文献紹介: Similarity-Based Reconstruction Loss for ...
Search
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
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
230
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
220
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
Evaluating LLM Reliability Across Facts, Evidence, and Cultures
yukiar
0
180
XDPerf: A High-Performance Traffic Generator Built with WASM and eBPF
takehaya
1
300
RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent
satai
3
560
論文紹介: Understanding Epistemic Language with a Language-augmented Bayesian Theory of Mind
hisaokatsumi
0
170
最先端NLP 2026 論文紹介: Wait, Wait, Wait... Why Do Reasoning Models Loop? / SNLP Paper Review: Wait, Wait, Wait... Why Do Reasoning Models Loop?
tkng
0
240
COMETAを用いたデータ民主化運動の歴史
sazimai
0
250
MIRU2026 チュートリアル講演2:三次元データ処理の動向
nnchiba
6
4.9k
Google Cloud Next 2026 DM Recap Agentic Data Cloudを添えて / Google Cloud Next 2026 DM Recap
nnaka2992
0
140
SoftMatcha 2: 1兆語規模コーパスの超高速かつ柔らかい検索
e869120_sub
7
3.9k
Pretrain Where? Investigating How Pretraining Data Diversity Impacts Geospatial Foundation Model Performance
satai
3
130
[ACL 2026 Demo] Fast-MIA: Efficient and Scalable Membership Inference for LLMs
upura
0
130
Anthropic が提案する LLM の内部状態を自然言語で説明可能にした Natural Language Autoencoders / Natural Language Autoencoders Produce Unsupervised Explanations of LLM Activations
shunk031
0
260
Featured
See All Featured
Build your cross-platform service in a week with App Engine
jlugia
234
19k
Noah Learner - AI + Me: how we built a GSC Bulk Export data pipeline
techseoconnect
PRO
0
440
It's Worth the Effort
3n
188
29k
Getting science done with accelerated Python computing platforms
jacobtomlinson
2
480
The AI Search Optimization Roadmap by Aleyda Solis
aleyda
1
6.2k
Paper Plane
katiecoart
PRO
4
53k
Building Applications with DynamoDB
mza
96
7.2k
Navigating Weather and Climate Data
rabernat
0
520
Statistics for Hackers
jakevdp
799
230k
Ecommerce SEO: The Keys for Success Now & Beyond - #SERPConf2024
aleyda
1
2.2k
Why You Should Never Use an ORM
jnunemaker
PRO
61
10k
What does AI have to do with Human Rights?
axbom
PRO
1
2.4k
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