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
A Word-Complexity Lexicon and A Neural Readabil...
Search
onizuka laboratory
December 18, 2018
Research
0
130
A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification
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
Tweet
Share
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
72
Card-660: A Reliable Evaluation Framework for Rare Word Representation Models
onilab
0
37
Integrating Transformer and Paraphrase Rules for Sentence Simplification
onilab
0
61
An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation
onilab
0
57
Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints
onilab
0
100
Modeling Multi-turn Conversation with Deep Utterance Aggregation
onilab
0
98
Learning Semantic Sentence Embeddings using Pair-wise Discriminator
onilab
0
120
SGM: Sequence Generation Model for Multi-Label Classification
onilab
0
80
Other Decks in Research
See All in Research
An Open and Reproducible Deep Research Agent for Long-Form Question Answering
ikuyamada
0
280
Attaques quantiques sur Bitcoin : comment se protéger ?
rlifchitz
0
140
ドメイン知識がない領域での自然言語処理の始め方
hargon24
1
240
【SIGGRAPH Asia 2025】Lo-Fi Photograph with Lo-Fi Communication
toremolo72
0
120
svc-hook: hooking system calls on ARM64 by binary rewriting
retrage
1
100
Upgrading Multi-Agent Pathfinding for the Real World
kei18
0
230
When Learned Data Structures Meet Computer Vision
matsui_528
1
2.9k
ACL読み会2025: Can Language Models Reason about Individualistic Human Values and Preferences?
yukizenimoto
0
130
2026.01ウェビナー資料
elith
0
220
さまざまなAgent FrameworkとAIエージェントの評価
ymd65536
1
420
姫路市 -都市OSの「再実装」-
hopin
0
1.6k
[Devfest Incheon 2025] 모두를 위한 친절한 언어모델(LLM) 학습 가이드
beomi
2
1.4k
Featured
See All Featured
AI Search: Where Are We & What Can We Do About It?
aleyda
0
7k
Claude Code のすすめ
schroneko
67
210k
Jess Joyce - The Pitfalls of Following Frameworks
techseoconnect
PRO
1
67
Noah Learner - AI + Me: how we built a GSC Bulk Export data pipeline
techseoconnect
PRO
0
110
sira's awesome portfolio website redesign presentation
elsirapls
0
150
How to train your dragon (web standard)
notwaldorf
97
6.5k
JavaScript: Past, Present, and Future - NDC Porto 2020
reverentgeek
52
5.8k
Impact Scores and Hybrid Strategies: The future of link building
tamaranovitovic
0
200
Organizational Design Perspectives: An Ontology of Organizational Design Elements
kimpetersen
PRO
1
470
Taking LLMs out of the black box: A practical guide to human-in-the-loop distillation
inesmontani
PRO
3
2k
The Success of Rails: Ensuring Growth for the Next 100 Years
eileencodes
47
7.9k
Claude Code どこまでも/ Claude Code Everywhere
nwiizo
61
52k
Transcript
EMNLP A Word-Complexity Lexicon and A Neural Readability Ranking Model
2018/12/18 M1
• 2 • 15000 • SimplePPDB++
2
3 Complex Sentence The cat perched on the mat. Substitution
Generation perched : rested, sat Substitution Ranking #1 : sat, #2 : rested Complex Word Identification The cat perched on the mat. Simplification Sentence The cat sat on the mat.
$,52(% *60#94 -):3 • 60 • $;! '
. • foolishness7 vs folly1 • 60 foolishness • Google Ngram Corpus foolishness/;! • PPDB"&2272 • 21%60 8160 • 14%/;! 760 4 +2
- • Google Ngram Corpus • Wo 15000 • 11
L • 6 5 6 • e p bug n d • C Wo c • 1000 i 2-2.5h • 1 5-7 L • m l 5
- C 2 • 3% • L 0.55 → 0.64
• • ≦0.5 47% • ≦1.0 78% • ≦1.5 93% 6
2 7
• ,/+*23.0! •
SemEval2012$! "% • )-2*15Candidates • $! "% • %'&(30Target300Candidate • #% 171Target1710Candidate 8 TEXT When you think about it, that’s pretty terrible. Target terrible Candidates bad, awful, deplorable
9 P@1 1 S all binning WC R 15000
• PPDB P Ranking model • PPDB • • •
+ + + • PPDB D • 10B S 10
+ 11 SimplePPDB++
Target Candidate • 100 Target Candidate • 2 • Candidate
G • SimplePPDB++ 12
13
• n Target • PPs Candidate • MAP Candidate • P@1 Top1
I • SemEval2016 CWIG3G2 • C WC 14
15
• 2'"#( & • SOTA% • 15000'"#(
• !*$ CWI) • SimplePPDB++ 16