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:MoverScore: Text Generation Evaluating wit...
Search
Taichi Aida
October 14, 2019
Technology
0
520
文献紹介:MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance
Taichi Aida
October 14, 2019
Tweet
Share
More Decks by Taichi Aida
See All by Taichi Aida
PhD Defence: Considering Temporal and Contextual Information for Lexical Semantic Change Detection
a1da4
1
210
文献紹介:A Multidimensional Framework for Evaluating Lexical Semantic Change with Social Science Applications
a1da4
1
300
YANS2024:目指せ国際会議!「ネットワーキングの極意(国際会議編)」
a1da4
0
230
言語処理学会30周年記念事業留学支援交流会@YANS2024:「学生のための短期留学」
a1da4
1
360
新入生向けチュートリアル:文献のサーベイv2
a1da4
15
10k
文献紹介:Isotropic Representation Can Improve Zero-Shot Cross-Lingual Transfer on Multilingual Language Models
a1da4
0
180
文献紹介:WhitenedCSE: Whitening-based Contrastive Learning of Sentence Embeddings
a1da4
1
290
文献紹介:On the Transformation of Latent Space in Fine-Tuned NLP Models
a1da4
0
110
新入生向けチュートリアル:文献のサーベイ
a1da4
0
490
Other Decks in Technology
See All in Technology
LLM翻訳ツールの開発と海外のお客様対応等への社内導入事例
gree_tech
PRO
0
430
つくって納得、つかって実感! 大規模言語モデルことはじめ
recruitengineers
PRO
32
12k
ここ一年のCCoEとしてのAWSコスト最適化を振り返る / CCoE AWS Cost Optimization devio2025
masahirokawahara
1
1.2k
DDD集約とサービスコンテキスト境界との関係性
pandayumi
2
220
Grafana MCPサーバーによるAIエージェント経由でのGrafanaダッシュボード動的生成
hamadakoji
1
1k
DuckDB-Wasmを使って ブラウザ上でRDBMSを動かす
hacusk
1
140
異業種出身エンジニアが気づいた、転向して十数年経っても変わらない自分の武器とは
macnekoayu
0
260
見てわかるテスト駆動開発
recruitengineers
PRO
6
2.4k
【初心者向け】ローカルLLMの色々な動かし方まとめ
aratako
4
2.5k
Snowflakeの生成AI機能を活用したデータ分析アプリの作成 〜Cortex AnalystとCortex Searchの活用とStreamlitアプリでの利用〜
nayuts
0
150
新規案件の立ち上げ専門チームから見たAI駆動開発の始め方
shuyakinjo
0
640
なぜスクラムはこうなったのか?歴史が教えてくれたこと/Shall we explore the roots of Scrum
sanogemaru
0
160
Featured
See All Featured
CSS Pre-Processors: Stylus, Less & Sass
bermonpainter
358
30k
Optimising Largest Contentful Paint
csswizardry
37
3.4k
Rebuilding a faster, lazier Slack
samanthasiow
83
9.1k
Building Flexible Design Systems
yeseniaperezcruz
328
39k
Raft: Consensus for Rubyists
vanstee
140
7.1k
A Modern Web Designer's Workflow
chriscoyier
696
190k
Rails Girls Zürich Keynote
gr2m
95
14k
Keith and Marios Guide to Fast Websites
keithpitt
411
22k
Building a Modern Day E-commerce SEO Strategy
aleyda
43
7.5k
How to train your dragon (web standard)
notwaldorf
96
6.2k
Designing Experiences People Love
moore
142
24k
Optimizing for Happiness
mojombo
379
70k
Transcript
จݙհʢʣ MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth
Mover Distance Wei Zhao† , Maxime Peyrard† , Fei Liu‡ , Yang Gao† , Christian M. Meyer† , Steffen Eger† EMNLP2019 Ԭٕज़Պֶେֶ ࣗવݴޠॲཧݚڀࣨɹ ૬ాɹଠҰ
Abstract • ੜͷλεΫʹ͓͍ͯɺؤڧͳධՁईΛௐࠪ • จ຺Λߟྀͨ͠୯ޠࢄදݱ ͱ Word Mover’s Distance ͷΈ߹Θ͕ͤ࠷ྑ͔ͬͨ
• ιʔείʔυΛެ։ɹɹɹɹɹɹɹɹɹɹɹɹɹɹɹ https://github.com/AIPHES/emnlp19-moverscore 2
Related work • ৭ʑͳධՁख๏ʢ1ʣ • ཁɿROUGE(Lin 2004) • ػց༁ɿBLEU(Papinemi 2002),
RUSE(Shimanaka 2018) • Image CaptioningɿBLEU, CIDEr(Vedantam 2015), SPICE(Anderson 2016) 3 #-&6͔ͳ͍
Related work • ৭ʑͳධՁख๏ʢ2ʣ • ҙຯతྨࣅɿ “BERTScore”(Zhang 2019) • ༁ɿڭࢣ͋Γɾڭࢣͳ͠
BERT ࢄදݱ(Mathur 2019) • ཁɺΤοηΠ࠾ɿELMo + Sentence Mover’s Simirality(Clark 2019) 4 จ຺Λߟྀͨ͠ࢄදݱ $POUFYUVBMJ[FESFQSFTFOUBUJPO Λ༻͍Δख๏͕૿͖͑ͯͨ ࣮ݧͷ#BTFMJOFʹग़͖ͯ·͢
Method • ༷ʑͳੜλεΫΛධՁͰ͖Δࢦඪ(MoverScore)Λௐࠪ • ੜจͱࢀরจͷྨࣅʢʁʣΛଌΔ • จ຺Λߟྀͨ͠ࢄදݱɿBERT, ELMo • ग़ྗจͱࢀরจͷҙຯతڑɿWord
Mover's Distance 5
Method • MoverScore Variations • Granularityɿn-gram (n=1, 2, size-of-sentence) •
Embeddingɿword2vec, BERT, ELMo • Fine-tuningɿMultiNLI, QANLI, QQP • Aggregationɿpower means, routing mechanism 6 /-* 1BSBQISBTJOH #&35 &-.P #&35
Method • MoverScore Variations • Granularityɿn-gram (n=1, 2, size-of-sentence) •
Embeddingɿword2vec, BERT, ELMo • Fine-tuningɿMultiNLI, QANLI, QQP • Aggregationɿpower means, routing mechanism 7 #&35 &-.P
Method • Aggregation ʢ౷߹ํ๏ʣ • จ຺Λߟྀͨ͠ࢄදݱɿBERT, ELMo • ֤୯ޠ֤͔ΒͦΕͧΕҟͳΔϕΫτϧ͕͞ΕΔ •
Power MeansɿฏۉΛऔΓ ( )ɺconcat • Routing Mechanismɿৄ͘͠(Zhang 2018) p p = 1, ± ∞ 8
Method • ग़ྗจͱࢀরจͷҙຯతڑ • Word Mover's Distance (WMD) • Sentence
Mover's Distance (SMD) • ઌ΄ͲͷΈ߹ΘͤΛɺWMD, SMD ͦΕͧΕͰݕূ͢Δ 9
Experiment • Tasks • ػց༁ • ཁ • ରʢλεΫࢤʣ •
Image Captioning 10 ʢࢀরจɺෳͷγεςϜʹΑΔग़ྗจʣͷϖΞ γεςϜͷग़ྗจʹਓखධՁ͕͞Ε͍ͯΔ ʲධՁࢦඪɺMoverScore ͰΔ͜ͱʳ ɾγεςϜͷग़ྗจΛධՁ ɾਓखධՁͱͷ૬ؔΛݟΔ
Experiment • ػց༁ • DatasetɿWMT2017 • ࢀՃγεςϜͷग़ྗจʹɺ࠷Ͱ15ਓͷਓखධՁ • BaselinesɿSentBLEU, METEOR++,
RUSE, BERTScore(Zhang 2019) 11
Result • WMD+BERT+MNLI+PMeans ͕ Baseline Λ্ճΔ 12
Result • Sentence Representation Ͱใ͕ࣦΘΕΔʁ 13
Experiment • ཁ • DatasetɿTAC-2008, TAC-2009 • Responsivenessɿ༰ʴจ๏తͳ࣭ • Pyramidɿࢀরจʹؚ·ΕΔॏཁͳ༰͕ͲΕ͚ͩଟ͘Χόʔ͞
Ε͍ͯΔ͔ • BaselinesɿROUGE-1, ROUGE-2, (Peyrard 2017), BERTScore(Zhang 2019) S3 best 14 ڭࢣ͋ΓͷධՁࢦඪ
Result • WMD+BERT+MNLI+PMeans Ͱ Baselines Λ্ճΔ 15
Experiment • ରʢλεΫࢤʣ • DatasetɿBAGEL, SFHOTEL • Informativeness (Inf)ɿఏڙ͢Δใྔ •
Naturalness (Nat)ɿਓͷԠͷۙ͞ • Quality (Qual)ɿྲྀெੑɾจ๏ • BaselinesɿBLEU, METEOR, BERTScore(Zhang 2019) 16
Result • શମతʹ૬͕͍͕ؔɺఏҊख๏ͦͷதͰߴ͍ํ 17
Experiment • Image Captioning • DatasetɿMSCOCO • M1 ~ M5
ͷධՁ͕͋Δ • ࠓճɺશମͷ࣭ʹؔ͢ΔM1, M2 Λ࠾༻ • BaselinesɿCIDEr, SPICE, METEOR, LEIC(Cui 2018), BERTScore(Zhang 2019) 18 ڭࢣ͋ΓͷධՁࢦඪ
Result • Baseline ͷ LEIC ʹྼΔ͕ɺͦΕͰߴ͍૬ؔΛࣔ͢ 19 M: BERT fine-tuning
ʹ MultiNLI Λ༻ P: ELMo / BERT ͷ౷߹ (Aggregation) ʹ Power Means Λ༻
Discussion • ࣮ݧͷ Baseline ͱͯ͠ग़͖ͯͨ BERTScore ͱͷൺֱ 20
Discussion • ࣮ݧͷ Baseline ͱͯ͠ग़͖ͯͨ BERTScore ͱͷൺֱ 21 One-to-one ͷڧ͍
alignment Many-to-one ͷऑ͍ alignment WMD Ͱదͳڑ ͕औΕ͍ͯΔ
Discussion • ػց༁ͰਓखධՁͷߴ͍ͷ(good)ͱ͍ͷ(bad)ͷɹ 2ͭʹ͚ɺΛௐࠪ • ൺֱର • Baseline: SentBLEU •
Proposal: MoverScore(WMD+BERT) 22
Discussion • SentBLEU ਓखධՁ͕ྑͯ͘தఔͷՕॴʹଟ͘ • MoverScore ៉ྷʹ2ͭͷۃΛදݱͰ͖͍ͯΔ 23
Conclusion • ੜλεΫͷڭࢣͳ͠ධՁࢦඪΛఏҊ • 4ͭͷੜλεΫͰ Baselines Λ ͑Δ/ഭΔ ݁Ռʹ •
ιʔείʔυΛެ։ɹɹɹɹɹɹɹɹɹɹɹɹɹɹɹ https://github.com/AIPHES/emnlp19-moverscore 24