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文献紹介:MoverScore: Text Generation Evaluating wit...
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Taichi Aida
October 14, 2019
Technology
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文献紹介:MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance
Taichi Aida
October 14, 2019
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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