$30 off During Our Annual Pro Sale. View Details »
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
文献紹介: Decomposable Neural Paraphrase Generation
Search
Yumeto Inaoka
July 23, 2019
Research
0
240
文献紹介: Decomposable Neural Paraphrase Generation
2019/07/23の文献紹介で発表
Yumeto Inaoka
July 23, 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
200
文献紹介: Open Domain Web Keyphrase Extraction Beyond Language Modeling
yumeto
0
250
文献紹介: Self-Supervised_Neural_Machine_Translation
yumeto
0
170
文献紹介: Comparing and Developing Tools to Measure the Readability of Domain-Specific Texts
yumeto
0
180
文献紹介: PAWS: Paraphrase Adversaries from Word Scrambling
yumeto
0
170
文献紹介: Beyond BLEU: Training Neural Machine Translation with Semantic Similarity
yumeto
0
290
文献紹介: EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing
yumeto
0
360
文献紹介: Analyzing the Limitations of Cross-lingual Word Embedding Mappings
yumeto
0
240
文献紹介: Similarity-Based Reconstruction Loss for Meaning Representation
yumeto
1
230
Other Decks in Research
See All in Research
Community Driveプロジェクト(CDPJ)の中間報告
smartfukushilab1
0
110
SkySense V2: A Unified Foundation Model for Multi-modal Remote Sensing
satai
3
220
AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse label data
satai
3
590
単施設でできる臨床研究の考え方
shuntaros
0
3.3k
スキマバイトサービスにおける現場起点でのデザインアプローチ
yoshioshingyouji
0
270
大規模言語モデルにおけるData-Centric AIと合成データの活用 / Data-Centric AI and Synthetic Data in Large Language Models
tsurubee
1
460
[RSJ25] Enhancing VLA Performance in Understanding and Executing Free-form Instructions via Visual Prompt-based Paraphrasing
keio_smilab
PRO
0
190
POI: Proof of Identity
katsyoshi
0
120
snlp2025_prevent_llm_spikes
takase
0
420
ロボット学習における大規模検索技術の展開と応用
denkiwakame
1
180
Remote sensing × Multi-modal meta survey
satai
4
650
地域丸ごとデイサービス「Go トレ」の紹介
smartfukushilab1
0
700
Featured
See All Featured
Un-Boring Meetings
codingconduct
0
160
It's Worth the Effort
3n
187
29k
The SEO identity crisis: Don't let AI make you average
varn
0
36
Redefining SEO in the New Era of Traffic Generation
szymonslowik
1
170
Conquering PDFs: document understanding beyond plain text
inesmontani
PRO
4
2.1k
Building a Scalable Design System with Sketch
lauravandoore
463
34k
Leveraging LLMs for student feedback in introductory data science courses - posit::conf(2025)
minecr
0
88
Skip the Path - Find Your Career Trail
mkilby
0
27
[Rails World 2023 - Day 1 Closing Keynote] - The Magic of Rails
eileencodes
37
2.7k
Crafting Experiences
bethany
0
22
Scaling GitHub
holman
464
140k
Money Talks: Using Revenue to Get Sh*t Done
nikkihalliwell
0
120
Transcript
Decomposable Neural Paraphrase Generation
https://arxiv.org/abs/1906.09741
• • • •
• • •
•
• • •
• • •
• •
• • = [1 , … , ] • =
[1 , … , ]
• • • ℎ = BiLSTM( ; ℎ−1 , ℎ+1
) • = LSTM ℎ , −1 ; −1 • = GumbelSoftmax( , )
• • = − encoderz (, ) • 1:−1 ,
= − encoderz , 1:−1
• • 1:−1 , = σ 1:−1 , ( |1:−1
, )
• 0 , 1 • = LSTM 0 ; 1
; −1 ; −1 • 1:−1 , = GumbelSoftmax ,
• • ∗ = 0 ∗ = 1
• • ℒ = σ=1 log 1:−1 , + σ=1
log ∗ + σ=1 log ( ∗ 1:−1 ,
• • •
• •
•
• •
• •
• •
• •
• •
• • From 1(best) to 4(worst)
• • • • •