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
Personalizing Lexical Simplification
Search
onizuka laboratory
October 17, 2018
Research
84
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
Personalizing Lexical Simplification
弊研究室で行なったCOLING2018読み会の発表資料です。
onizuka laboratory
October 17, 2018
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
94
Card-660: A Reliable Evaluation Framework for Rare Word Representation Models
onilab
0
44
A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification
onilab
0
160
Integrating Transformer and Paraphrase Rules for Sentence Simplification
onilab
0
70
An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation
onilab
0
68
Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints
onilab
0
110
Modeling Multi-turn Conversation with Deep Utterance Aggregation
onilab
0
100
Learning Semantic Sentence Embeddings using Pair-wise Discriminator
onilab
0
130
Other Decks in Research
See All in Research
ふとした出会いで生まれたSkillが、 社内利用1位になるまで
mikimhk
21
24k
敵対生成プロンプト同時探索による内省型プロンプト最適化
kinoue_smarthr
0
410
Data Visualization Tools in the Age of AI
flekschas
0
200
[最先端NLP勉強会2026] Agentic Rubrics as Contextual Verifiers for SWE Agents
rfujii
1
350
実例から見るLLMのマンガ理解:実務VQAタスクによる長期的文脈と視覚情報の定性評価
kzmssk
0
110
Visual SLAM未来予測 / Future Prediction in Visual SLAM
koide3
1
1k
SAKURAONE:An Open Ethernet-based AI HPC System And Its Observed Workload Dynamicsin a Single-Tenant LLM Development Environment
yuukit
1
590
データサイエンティストの就労意識~2015 → 2026 一般(個人)会員アンケートより
datascientistsociety
PRO
0
770
人間中心の意思決定支援AI
yukinobaba
PRO
7
4k
Sleuthcon Keynote - How Cybercriminals (ab)use AI
fr0gger
0
320
適応的スパムフィルタのための軽量な類似メッセージカウンタ / jsai2026-adaptive-spam-filter
monochromegane
0
5.4k
Language and AI
ayaniwa
0
230
Featured
See All Featured
AI Search: Where Are We & What Can We Do About It?
aleyda
0
7.9k
Documentation Writing (for coders)
carmenintech
77
5.5k
The Curse of the Amulet
leimatthew05
2
14k
A Soul's Torment
seathinner
7
3.6k
Everyday Curiosity
cassininazir
0
310
AI: The stuff that nobody shows you
jnunemaker
PRO
9
980
Self-Hosted WebAssembly Runtime for Runtime-Neutral Checkpoint/Restore in Edge–Cloud Continuum
chikuwait
0
780
[SF Ruby Conf 2025] Rails X
palkan
2
1.4k
GraphQLとの向き合い方2022年版
quramy
50
15k
How to Think Like a Performance Engineer
csswizardry
28
2.8k
Information Architects: The Missing Link in Design Systems
soysaucechin
1
1.1k
Building Experiences: Design Systems, User Experience, and Full Site Editing
marktimemedia
0
600
Transcript
COLING Personalizing Lexical Simplification 2018/10/17 M1
;' • Lexical SimplificationLS1+50)8% • 1+50&7 6 • /20!-:
LS,* • -:. 9"( • $1 50Target # • $1 50Candidate43 2
• Lexical SimplificationLS • • •
• • • • • 3
4 Complex Sentence The cat perched on the mat. Substitution
Generation perched : rested, sat, alighted Substitution Ranking #1 : sat, #2 : rested Substitution Selection perched : rested, sat Complex Word Identification The cat perched on the mat. Simplification Sentence The cat sat on the mat.
Complex Word IdentificationCWI • SemEval2016 • 1
• 20 ! 0.244 • 5
23$" /40#:5 LS,* Complex Word Identification • 4-1+80/:5 )! •
%9 Target. Substitution RankingSubstitution Selection ? • '7(801+80 • 6& 6
(%$&)94 • 15+!*'% • 12000@=50#"!: 1. / 2. /
3. / 3?8 or 3?D 4. 6. . 5. A2@=3? . Low Proficiency 074+ 218@=;-CE<41% High Proficiency ,74+ 218@=;-CE<75% 7 1-4> 5B@
8 Targetavoid BenchLS
" #-4'* ( • 40+# !)% •
F& • #- +#,#- +#$ 9
3=#4 • nilBaseline • Target 86( 7. • Candidate
86( +: • gold • 27.+:*, • +:Target7.Candidate'; • auto0-"& • 40586</9/! %$ • ! 7.+: 1) 10
)%#+ • Precision • !- & • !-,'"*
• ($,'"* • Accuracy • "*,'. • "* • ($,'"* • Readability • "* ,' !- 11
• • Candidate BenchLS •
• • Candidate • 12
13 Candidate • nil
• auto • gold
14 Candidate • nil • auto
• gold
• #+! • 4'). • 40*%
• , " • -* *%$( • &*% $( 15
• Ranking Selection • "!#%' • &) •
( 34.81%$ • 16