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
1
190
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
Tweet
Share
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
140
文献紹介: Open Domain Web Keyphrase Extraction Beyond Language Modeling
yumeto
0
180
文献紹介: Self-Supervised_Neural_Machine_Translation
yumeto
0
130
文献紹介: Comparing and Developing Tools to Measure the Readability of Domain-Specific Texts
yumeto
0
130
文献紹介: PAWS: Paraphrase Adversaries from Word Scrambling
yumeto
0
110
文献紹介: Beyond BLEU: Training Neural Machine Translation with Semantic Similarity
yumeto
0
230
文献紹介: EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing
yumeto
0
290
文献紹介: Decomposable Neural Paraphrase Generation
yumeto
0
190
文献紹介: Analyzing the Limitations of Cross-lingual Word Embedding Mappings
yumeto
0
190
Other Decks in Research
See All in Research
LiDARとカメラのセンサーフュージョンによる点群からのノイズ除去
kentaitakura
0
230
2024/10/30 産総研AIセミナー発表資料
keisuke198619
1
390
さんかくのテスト.pdf
sankaku0724
0
640
湯村研究室の紹介2024 / yumulab2024
yumulab
0
360
文化が形作る音楽推薦の消費と、その逆
kuri8ive
0
220
CVPR2024 参加報告
kwchrk
0
140
20240918 交通くまもとーく 未来の鉄道網編(太田恒平)
trafficbrain
0
420
LLM時代にLabは何をすべきか聞いて回った1年間
hargon24
1
580
2038年問題が思ったよりヤバい。検出ツールを作って脅威性評価してみた論文 | Kansai Open Forum 2024
ran350
8
3.7k
渋谷Well-beingアンケート調査結果
shibuyasmartcityassociation
0
350
テキストマイニングことはじめー基本的な考え方からメディアディスコース研究への応用まで
langstat
1
160
Geospecific View Generation - Geometry-Context Aware High-resolution Ground View Inference from Satellite Views
satai
2
140
Featured
See All Featured
Building an army of robots
kneath
302
44k
Put a Button on it: Removing Barriers to Going Fast.
kastner
59
3.6k
I Don’t Have Time: Getting Over the Fear to Launch Your Podcast
jcasabona
29
2.1k
BBQ
matthewcrist
85
9.4k
How To Stay Up To Date on Web Technology
chriscoyier
790
250k
Statistics for Hackers
jakevdp
797
220k
Why Our Code Smells
bkeepers
PRO
335
57k
Helping Users Find Their Own Way: Creating Modern Search Experiences
danielanewman
29
2.4k
How GitHub (no longer) Works
holman
312
140k
GitHub's CSS Performance
jonrohan
1030
460k
Bootstrapping a Software Product
garrettdimon
PRO
305
110k
YesSQL, Process and Tooling at Scale
rocio
170
14k
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