fl u知能学会研究会資料
SIG-SLUD-C003-20

ϚϧνϞʔμϧ৘ใΛ༻͍ͨෳ਺ਓٞ࿦ͷ඼࣭ධՁMultimodal Argumentation Quality Assessmentin Multi-party ConversationsԘా ࠻ 1∗
Tsukasa Shiota1

ౢా ࿨޹ 1
Kazutaka Shimada1‫۝‬भ޻‫ۀ‬େֶେֶӃ ৘ใ޻ֶ෎ ઌ୺৘ใ޻ֶઐ߈
Department of Advanced Informatics, Kyushu Institute of Technology
11

Abstract: Recently, argumentation quality assessment tasks have attracted a lot of attention inNLP. However, there are a few studies to challenge argumentation quality assessment, focusing
on synchronous argumentation. In this study, we build a multimodal multi-party argumentation
corpus that is annotated argumentation quality scores. We propose some automatic argumentationassessment methods and report the results of each method. We show that information to deal withshould be changed based on the target score to assess.

1͸͡Ίʹۙ೥ɼ‫ڭ‬ҭʹ͓͍ͯ໰୊‫ج‬൫‫ֶܕ‬श΍‫ڠ‬ௐֶश͕ίϛϡχέʔγϣϯೳྗͳͲ஌ࣝҎ֎ͷೳྗʢඇఆ‫ܗ‬஌ʣΛ‫܇‬࿅͢Δखஈͱͯ͠஫໨ΛूΊ͍ͯΔɽ‫ڭ‬ҭʹ͓͍ͯඇఆ‫ܗ‬஌Λ‫܇‬࿅͢Δํ๏ͷ 1 ͭʹ౼࿦΍߹ҙ‫ܗ‬੒Λߦ͏άϧʔϓσΟεΧογϣϯ͕͋Δɽֶߍ‫ݱ‬৔ͷ‫ڭ‬ࢣ͕͜ͷֶशํ๏Λಋೖ͢Δ৔߹ɼ1 ͭͷΫϥεʹσΟεΧογϣϯΛ࣮ࢪ͢Δάϧʔϓ͕ಉ࣌ʹෳ਺ଘࡏ͢Δ͜ͱʹͳΓɼશͯͷάϧʔϓɾ‫ݸ‬ਓͷೳྗ΍੒ՌΛ༷ʑͳ֯౓ͰධՁɾϑΟʔυόοΫ͢Δ͜ͱ͸ଟେͳ࿑ྗΛཁ͢ΔɽߋʹɼσΟεΧογϣϯͰ͸ਖ਼ղͷͳ͍՝୊ΛऔΓѻ͏ͨΊɼάϧʔϓ΍‫ݸ‬ਓʹରͯ͠ಋ͖ग़͞ΕΔධՁ͕ৗʹఆྔతͰ٬‫؍‬ੑ͕อূ͞Ε͍ͯΔͱ͸‫ݶ‬Βͳ͍ɽͦ͜Ͱɼάϧʔϓͷ༷ࢠΛ༷ʑͳϞμϦςΟͷ৘ใΛ֫ಘͯٞ͠࿦಺༰ͷ඼࣭ΛՄࢹԽ͢ΔγεςϜΛߏங͢Δ͜ͱ͕Ͱ͖Ε͹ɼධՁऀͷධՁ‫׆‬ಈͷෛ୲Λܰ‫͢ݮ‬Δ͜ͱ͕Ͱ͖Δͱߟ͑ΒΕΔɽࣗવ‫ॲޠݴ‬ཧͷ෼໺ʹ͓͍ͯ‫ڭ‬ҭԠ༻Λ໨తͱͨٞ͠࿦ͷ඼࣭ධՁͷ‫ͯ͠ͱྫڀݚ‬͸ Automated EssayScoringʢAESʣ[Ke 19] ͳͲ͕͋Δɽ͔͠͠ͳ͕Βɼ࿩͠‫ݴ‬༿ʹΑΔٞ࿦͸ߏ੒͕ॻ͖‫ݴ‬༿΄Ͳ໌֬Ͱ͸ͳ͘ɼ‫͍ݴ‬ཎΈͳͲΛ‫ؚ‬Ή‫ޠݴ‬ΛऔΓѻ͏ඞཁ͕͋ΔͳͲɼॻ͖‫ݴ‬༿ͱ͸ҟͳΓ༷ʑͳࠔ೉͕͞ଘࡏ͢ΔՄೳੑ͕͋Δɽ͞Βʹɼॻ͖‫ݴ‬༿ͷ࿦ड़ͷ඼࣭͸‫ޠݴ‬త৘ใͷΈΛ
ߟྀ͢Δ͜ͱͰਪఆ͢Δ͜ͱ͕ՄೳͰ͋Δ͕ɼ࿩͠‫ݴ‬༿ͷ௠ड़͸‫ݴ‬༿͔ΒղऍͰ͖Δ৘ใͷΈͰ͸ͳ͘ɼൃ∗ ࿈བྷઌɿ‫۝‬भ޻‫ۀ‬େֶେֶӃ ৘ใ޻ֶ෎ ઌ୺৘ใ޻ֶઐ߈
ɹɹɹɹɹɹ˟ 820-8502 ෱Ԭ‫ ݝ‬൧௩ࢢ ઒௡ 680-4
ɹɹɹɹɹɹ E-mail: t shiota@pluto.ai.kyutech.ac.jp‫ऀݴ‬ͷಈ࡞ͳͲͷඇ‫ޠݴ‬తཁૉ͔Β΋Ө‫ڹ‬Λड͚Δ͜ͱ͕஌ΒΕ͍ͯΔ [෢઒ 18]ɽͦ͜Ͱɼຊ‫Ͱڀݚ‬͸ٞ࿦ͷ඼࣭ධՁϥϕϧ͕஫ऍ෇͚͞ΕͨϚϧνϞʔμϧෳ਺ਓٞ࿦ίʔύεͷߏஙɼ͓Αͼ඼࣭ධՁϞσϧͷߏஙͱੑೳධՁΛ࣮ࢪ͢Δɽ2ؔ࿈‫ڀݚ‬ର໘‫ࣜܗ‬ͷٞ࿦Λର৅ͱͨ͠ίʔύε͸ෳ਺ଘࡏ͢
ΔɽZhang Β [Zhang 16] ͸‫ٕࣜڝ‬σΟϕʔτΛίʔύεͱͯ͠ߏங͠ɼ60%΄Ͳͷਖ਼ղ཰ͰউऀΛ༧ଌ͕ՄೳͰ͋Δ͜ͱΛใࠂ͍ͯ͠ΔɽྛΒ [ྛ 15] ͸ίϛϡχέʔγϣϯεΩϧΛࣗಈਪఆ͢Δ͜ͱΛ໨తͱͯ͠ɼ࠾༻ࢼ‫ݧ‬Λ໛ͨ͠σΟεΧογϣϯΛऩ࿥ͨ͠ίʔύεΛߏங͍ͯ͠Δɽ͜ͷίʔύεʹ͸ൃ࿩ͷॻ͖‫͜͠ى‬΍ಈ࡞ɾԻ੠σʔλɼ͓ΑͼίϛϡχέʔγϣϯεΩϧʹؔ࿈͢Δ 5 ͭͷ߲໨ʹ͍֤ͭͯٞ࿦ࢀՃऀΛධՁͨ݁͠Ռ͕ऩ࿥͞Ε͍ͯΔɽOlshefski Β [Olshefski 20]͸‫ࣨڭ‬ͷ‫ڠ‬ௐֶशͷ෼ੳΛ໨తͱͯ͠ɼߴߍͷࠃ‫ޠ‬ͷत‫ߦͰۀ‬ΘΕΔٞ࿦Λ 29 ର࿩ऩ࿥ͨ͠ίʔύεΛߏங͍ͯ͠Δɽ
1 અͰ΋ड़΂ͨ௨Γɼॻ͖‫ݴ‬༿ͷٞ࿦Λର৅ͱͨ͠඼࣭ਪఆ͸༷ʑߦΘΕ͍ͯΔɽ୅දతͳ΋ͷͱͯ͠͸ӳ‫ֶޠ‬शऀ͕‫ֶֶޠ‬शͷࡍʹࣥච͢ΔΤοηΠͷ࿦/ٞ࿦ͱ඼࣭είΞΛϖΞσʔλͱͨ͠ίʔύεΛߏங͠ɼٞ࿦ͷ‫͞ڧ‬΍ߏ੒ͳͲ༷ʑͳධՁ࣠ʹΑͬͯࣗಈ
ධՁ͢Δ AES [Ke 19] ͕͋ΔɽΠϯλϥΫγϣϯΛର৅ͱͨ͠‫Ͱڀݚ‬͸ਓ΍άϧʔϓͷύϑΥʔϚϯεΛධՁ͢Δख๏ΛఏҊ͢Δ‫ڀݚ‬͸਺ଟ͘ଘࡏ͍ͯ͠Δɽྫ

ٕ 116 ٕ͑͹ɼOkada Β [Okada 16] ͸ෳ਺ਓٞ࿦ͷࢀՃऀͷίϛϡχέʔγϣϯೳྗΛਪఆ͢ΔϚϧνϞʔμϧͳධՁϞσϧΛఏҊ͍ͯ͠Δɽଞʹ΋ɼAvci Β [Avci 16]
΍ Murray Β [Murray 18] ͸ٞ࿦ࢀՃऀ΍ٞ࿦ঢ়ଶ͔Β֫ಘͰ͖Δ৘ใΛ༻͍ͯάϧʔϓͷੑೳΛࣗಈධՁ͢Δख๏ΛఏҊ͍ͯ͠Δɽຊ‫ڀݚ‬͸ɼΠϯλϥΫγϣϯ‫ڀݚ‬ͷ஌‫ݟ‬ΛऔΓೖΕͳ͕Βɼࣗવ‫ॲޠݴ‬ཧͷ෼໺ͰऔΓѻ͏Α͏ͳٞ࿦ͷ඼࣭ਪఆλεΫΛෳ਺ਓٞ࿦ͷσʔλΛର৅ʹ࣮ࢪ͢Δɽ3σʔληοτͷߏஙද 1: ٞ࿦ηάϝϯτͷ඼࣭ͷධఆ࣠Ұཡ࣠֓ཁReGAGRGSٞ࿦͕ GA ͔Β GS Λຬ͍ͨͯ͠Δ͔ٞ࿦ͷ಺༰/ਐߦํ๏͕ద੾Ͱ‫ڐ‬༰Ͱ͖Δ͔ٞ࿦ͷ಺༰͕ٞ୊ͱؔ࿈ͨ͠΋ͷͰ͋Δ͔ٞ࿦ͷ಺༰͕಺ল͞Ε͍ͯΔ͔

EfCrEmClApAr

ٞ࿦͕ Cr ͔Β Ar Λຬ͍ͨͯ͠Δ͔ٞ࿦ͷ಺༰Λ৴པ͢Δ͜ͱ͕Ͱ͖Δ͔ٞ࿦ʹରͯ͠ΦʔϓϯϚΠϯυʹͳΕΔ͔ٞ࿦ͷ಺༰͕໌շ͔ٞ࿦ʹ͓͚Δ֤࿩ऀͷ‫ݴ‬ಈ͕‫ݐ‬ઃత͔࿦఺΍ࠜ‫ڌ‬ɼ݁࿦ͷྲྀΕ͕ཧղ͠΍͍͔͢ஶऀΒ [Shiota 20] ͸͜Ε·Ͱͷ‫ Ͱڀݚ‬4 ਓҰ૊ʹΑΔٞ࿦Λऩ࿥ͨ͠ෳ਺ਓର࿩ίʔύεΛߏங͍ͯ͠ΔɽຊίʔύεͰ͸ 2 ਓ͕ 1 ͭͷάϧʔϓͱͳΓɼ͋Δ໋୊ʹରͯ͠ࢍ੒ͱ൓ରͷཱ৔͔Β౼࿦͢Δର࿩ͱɼ2ʮ໌ྎੑʢClʣʯ
ʮଥ౰ੑʢApʣʯʮॱংੑʢArʣʯͱͳͬͭͷάϧʔϓ͕ೲಘͷ͍͘ં஭ҊΛಋ͘߹ҙ‫ܗ‬੒Λߦ
1ʹઌߦ‫ڀݚ‬Λ‫࡞ʹج‬੒ͨ͠ධՁ‫ج‬४ͷ͍֓ͯΔɽද͏ର࿩Λऩ࿥͍ͯ͠Δɽ1 ͭͷର࿩͸໿ 20 ෼Ͱɼ5 ͭཁΛࣔ͢ɽͷάϧʔϓʹΑΔ౼࿦͓Αͼ߹ҙ‫ܗ‬੒߹Θͤͯ 10 ର࿩֤άϧʔϓʹରͯ͠ୈࡾऀ 3 ໊ΛׂΓ౰ͯɼٞ࿦ηΛ‫ࡏݱ‬ऩ࿥͓ͯ͠Γɼ߹‫ܭ‬ର࿩࣌ؒ໿ 200 ෼ͱͳͬͯάϝϯτͷ඼࣭Λධఆ͢Δ͜ͱΛґཔͨ͠ɽධఆऀ͸͍Δɽର࿩Λ‫ه‬࿥ͨ͠ө૾ɾԻ੠σʔλΛ‫ʹج‬ɼ0.2 ඵٞ࿦ͷಈը͓ΑͼηάϝϯςʔγϣϯࡁΈͷൃ࿩ͷॻҎ্ͷແ੠۠ؒΛ۠੾Γͱ͢Δస‫ه‬୯ҐͰॻ͖‫͜͞ى‬͖‫͜͠ى‬σʔλΛӾཡ͠ͳ͕Βද 1 ͷධՁࢦඪʹ͍ͭΕͨൃ࿩ͷςΩετɼٞ࿦ࢀՃऀͷࠎ֨΍‫إ‬ͷ৘ใΛͯৄࡉͳઆ໌Λߦ͍ͬͯΔࢿྉΛ‫ʹج‬ٞ࿦ηάϝϯτ
OpenPose1 ɼOpenFace2 ʹΑͬͯղੳͨ͠࠲ඪ఺ɼͦ͠Λධఆͨ͠ɽ࡞‫ۀ‬खॱͱͯ͠͸͸͡ΊʹɼͦΕͧΕͷ3
ͯ Surfboard ʹΑͬͯղੳͨ͠ൃ࿩Ի੠ಛ௕ྔͱ͍ͬ
ैଐධՁ࣠Λ LʢLowʣɼMʢMiddleʣɼHʢHighʣͰධͨϚϧνϞʔμϧ৘ใ͕நग़ɼߏ଄Խ͞Εͨίʔύεͱͳ͍ͬͯΔɽ10 ର࿩ͷ߹‫ൃܭ‬࿩਺͸ 7,449 ൃ࿩Ͱ͋Δɽ Ձ͠ɼͦͷ‫ैޙ‬ଐධՁ࣠ͷείΞ෼෍Λ‫ʹج‬ओཁධՁ࣠ͷείΞΛ VLʢVery LowʣɼLɼMɼHɼVHʢVeryҰൠʹɼ1 ͭͷ໋୊ʹରͯ͠౼࿦/߹ҙ‫ܗ‬੒Λߦ͏৔ʣͷத͔Βܾఆ͢Δɽධఆ݁Ռʹ͍ͭͯҰக཰Λ
High߹ɼ༷ʑͳτϐοΫʹ͍ͭͯٞ࿦͢Δ͜ͱʹͳΔɽͦ‫ٻ‬ΊɼओཁධՁ͓࣠ΑͼैଐධՁ࣠ͷ݁Ռͷ৴པੑΛ͜Ͱຊ‫Ͱڀݚ‬͸ɼ࠾఺ର৅ͱ͢Δٞ࿦ΛτϐοΫηά֬ೝͨ͠ɽϝϯςʔγϣϯ͞Εͨର࿩ͷηάϝϯτʢҎ߱ɼٞ࿦ද 2 ʹ֤࣠ͷΫϦοϖϯυϧϑͷЋ܎਺Λࣔ͢ɽ͜ͷηάϝϯτʣͱ͠ɼٞ࿦ηάϝϯτʹ඼࣭είΞΛ෇܎਺͸ −1 ͔Β 1 ͷ࿈ଓ஋Ͱશͯͷई౓ͷҰக཰Λࢉग़༩ͨ͠σʔληοτΛߏங͢Δɽຊ‫Ͱڀݚ‬͸ɼ֤ର࿩Ͱ͖Δ΋ͷͰɼ‫ݧܦ‬తʹ 0.667 Ҏ্Ͱ͋Ε͹Ξϊςʔλʹ‫·ؚ‬ΕΔٞ࿦ηάϝϯτΛ֫ಘ͢ΔͨΊɼର࿩‫ڀݚ‬ʹΑΔධՁ͕Ұக͍ͯ͠Δͱ͞Ε͍ͯΔɽຊ‫ʹڀݚ‬Αʹ͓͍ͯ༗໊ͳίʔύεͷ 1 ͭͰ͋Δ AMI Corpus ͷΔ࣮‫Ͱݧ‬͸Ұͭͷ‫ج‬४Ͱ͋Δ 0.667 ʹ౸ୡ͢Δࢦඪ͸τϐοΫηάϝϯςʔγϣϯ [Xu 05] ΛࢀߟʹϝΠϯͳ͘ɼఏҊεΩʔϜͰ͸े෼ͳ৴པੑΛ༗ͨ͠ධఆ݁τϐοΫͷ‫ڥ‬քΛΞϊςʔγϣϯ͠ɼ࠾఺ର৅ͱͳΔՌΛಘΔ͜ͱ͸Ͱ͖ͳ͍͜ͱΛ֬ೝͨ͠ɽॻ͖‫ݴ‬༿ͷٞ࿦ηάϝϯτΛ 178 ݅Λ֫ಘͨ͠ɽςΩετʹରͯ͠ಉ༷ͷཧ࿦Λϕʔεʹͨ͠ධఆΛ࣮࣍ʹɼࣗવ‫ʹޠݴ‬ΑΔٞ࿦ͷ‫ࢉܭ‬࿦త඼࣭ධՁʹؔࢪ͠ɼ෼ੳ݁ՌΛใࠂ͍ͯ͠Δ࿦จ [Wachsmuth 17a]͢Δཧ࿦ [Wachsmuth 17b] Λ‫ʹج‬ධఆ͓࣠Αͼ‫ج‬४Λ͕ଘࡏ͢Δɽ͜ͷ‫͍͓ͯʹڀݚ‬΋Ϋϥ΢υϫʔΧʔʹ࡞੒͠ɼධఆ࡞‫ۀ‬Λ࣮ࢪ͢ΔɽWachsmuth Βͷఆٛ͢ΑΔධఆ݁ՌͷΫϦοϖϯυϧϑЋ܎਺͸-0.27ʙ0.53Δ෼ྨମ‫Ͱܥ‬͸ɼٞ࿦ͷ඼࣭͸ʮ߹ཧੑʢReʣʯ͓Αͼͱᮢ஋ʹ౸ୡ͍ͯ͠ͳ͍ɽ·ͨɼઌߦ‫Ͱڀݚ‬͸ΫϦοʮ༗ޮੑʢEfʣʯͷ 2 छྨͷओཁධՁ࣠ͱͦΕΒʹ෇ਵ͢ϖϯυϧϑЋ܎਺͕ 0 ΛԼճΔ͜ͱ΋͋Γɼ‫ۮ‬વΑΓΔैଐධՁ࣠ͰධՁ͢Δ͜ͱ͕Ͱ͖ΔɽͦΕͧΕʹର΋ߴ͍֬཰ͰΞϊςʔλؒͰͷෆҰக͕ൃੜ͍ͯ͠Δɽʮؔ࿈ੑʢGRʣʯʮॆ͢ΔैଐධՁ࣠͸ʮ༰ೝੑʢGAʣʯҰํͰɼຊ‫ڀݚ‬ͷධఆ݁Ռ͸ͦΕͧΕͷධՁ࣠ͷΫϦοʮ৘ಈੑʢEmʣʯ଍ੑʢGSʣʯɼ͓Αͼʮ৴༻ੑʢCrʣʯϖϯυϧϑͷЋ܎਺͕ 0 ΛԼճ͍ͬͯΔ࣠͸ଘࡏ͠ͳ
1 https://github.com/CMU-Perceptual-Computing͍ͨΊɼΞϊςʔλؒͰ‫ۮ‬વͷ֬཰Λ௒͑ͨෆҰக͕Lab/openpose
2 https://github.com/TadasBaltrusaitis/OpenFaceൃੜ͍ͯ͠ͳ͍͜ͱ͕อূ͞Ε͍ͯΔɽ͔͠͠ɼ͜Ε
3 https://github.com/novoic/surfboard

ٕ 117 ٕߏங͢ΔɽSVM Ͱ͸‫ྻܥ‬σʔλΛೖྗ͢Δ͜ͱ͸Ͱ͖ͳ͍ͨΊɼຊ‫Ͱڀݚ‬͸ൃ࿩ϕΫτϧͷ֤ཁૉͷ࣌ؒํ޲ͷฏ‫ۉ‬஋͓Αͼඪ४ภࠩΛࢉग़͠ɼͦΕΛٞ࿦ηάϝϯτͷϕΫτϧͱ͢ΔɽͦͷϕΫτϧΛೖྗͱͯ͠ɼର৅ͱͳΔධՁ࣠ͷ඼࣭είΞ ŷdim Λग़ྗ͢Δɽද 2: ֤ධఆ࣠ͷΫϦοϖϯυϧϑЋ܎਺

Re0.151

GA0.087

GR0.029GS
0.128--Ef
0.135

Cr
0.032

Em
0.038

Cl
0.017

Ap
0.076

Ar
0.155

4.3
ΒͷҰக཰΋·ͨෆे෼ͳͨΊɼࠓ‫ޙ‬͸ΑΓ৴པੑͷߴ͍ධఆ݁ՌΛ֫ಘ͢ΔΞϊςʔγϣϯख๏ͷ։ൃ͕ඞཁͰ͋Δɽͳ͓ɼຊ‫Ͱڀݚ‬ར༻͍ͯ͠ΔίʔύεͱධఆείΞ͸४උ͕੔͍࣍ୈຊ‫ࣨڀݚ‬ͷϗʔϜϖʔδ4ʹͯެ։༧ఆͰ͋Δɽ

SVM Λ༻͍ͨ඼࣭ධՁϞσϧͰ͸֤ൃ࿩ϕΫτϧͷ֤ཁૉΛฏ‫ۉ‬஋͓Αͼඪ४ภࠩͰ࣌ؒํ޲ʹ৞ΈࠐΜͰ͍ΔͨΊɼ֤ൃ࿩ͷ‫ྻܥ‬৘ใΛे෼ʹଊ͑Δ͜ͱ͕
Ͱ͖ͳ͍Մೳੑ͕͋Δɽͦ͜ͰɼຊઅͰ͸ LSTM Λ༻͍ͨ඼࣭ධՁϞσϧΛఏҊ͢Δɽೖྗ ui Λड͚औͬͨ࣌ࠁ i ʹ͓͚Δ LSTM ͷϢχοτ͸ҎԼͷΑ͏ʹ‫͞ࢉܭ‬ΕΔɽ

඼࣭ධՁख๏

4

hi = LST M (ui , hi−1 , ci−1 )ຊઅͰ͸ɼߏஙͨ͠σʔληοτΛର৅ʹٞ࿦ηάϝϯτͷ඼࣭ΛࣗಈධՁ͢ΔϞσϧʹ͍ͭͯղઆ͢Δɽ͸͡Ίʹɼຊ‫͚͓ʹڀݚ‬Δ඼࣭ධՁλεΫΛ‫ࣜܗ‬Խ͠ɼͦͷ‫ ޙ‬SVM ΍ਂ૚ֶशΛ༻͍ͨ඼࣭ධՁϞσϧΛఏҊ͢Δɽ

4.1඼࣭ධՁλεΫͷ‫ࣜܗ‬Խٞ࿦ηάϝϯτ S ͸ൃ࿩ϕΫτϧ‫ ྻܥ‬U = {u1 , u2 , ...,uN } Λ༗͍ͯ͠Δɽ͜͜ͰɼN ͸ٞ࿦ηάϝϯτ S ʹଐ͢Δൃ࿩ͷ਺Ͱ͋Γɼui ͸ٞ࿦ηάϝϯτ S ͷ i ൪໨ͷൃ࿩ϕΫτϧͰ͋Δɽൃ࿩ϕΫτϧ ui ͸ҎԼͷ‫ܗ‬ࣜͰද‫͞ݱ‬ΕΔɽ

ui = [spi ; ti ; bi ; f i ; ai ](1)

spi ͸ i ൪໨ͷൃ࿩ͷ࿩ऀ͕ i − 1 ൪໨ͷൃ࿩ͷ࿩ऀͱҟͳΔ͔൱͔Λද͢਺ɼti ͸ i ൪໨ͷൃ࿩ͷςΩετ৘ใΛද‫͢ݱ‬ΔϕΫτϧɼbi ͸ i ൪໨ͷൃ࿩தͷಈ࡞৘ใΛද‫͢ݱ‬ΔϕΫτϧɼf i ͸ i ൪໨ͷൃ࿩தͷ‫إ‬৘ใΛද‫͢ݱ‬ΔϕΫτϧɼai ͸ i ൪໨ͷൃ࿩ͷԻ੠৘ใ[ ;ɾ] ͸ϕΫτϧͷΛද‫͢ݱ‬ΔϕΫτϧͰ͋Δɽ·ͨɼɾ࿈݁Λද͢ɽͭ·Γɼ඼࣭ධՁλεΫ͸ٞ࿦ηάϝϯτͷ‫ޠݴ‬ɾඇ‫ޠݴ‬৘ใΛ༗͢Δൃ࿩ϕΫτϧ‫ ྻܥ‬U Λೖྗͱͯ͠ड͚औΓɼࢦఆͷ࣠ͷείΞ ydim Λਪఆ͢ΔλεΫͱͳΔɽ

4.2LSTM(2)্‫ه‬ͷखॱΛ‫܁‬Γฦ͠ɼٞ࿦ηάϝϯτʹଐ͢Δશͯͷൃ࿩ϕΫτϧ͕ೖྗ͞Εͨ LSTM ͷ࠷ऴঢ়ଶ hN Λ֫ಘ͢Δɽຊ‫Ͱڀݚ‬͸͜ͷ hN Λٞ࿦ηάϝϯτͷ෼ࢄද‫͠ͳݟͱݱ‬ɼग़ྗϥϕϧͷ༧ଌ֬཰෼෍ Ŷdim Λ‫ٻ‬ΊΔɽŶdim = softmax(W s hN + bs )(3)

W s ɼbs ͸ֶशύϥϝʔλɼsoftmax() ͸ιϑτϚοΫεؔ਺Λද͢ɽ࠷‫ʹޙ‬ɼ෼෍ͷ࠷େ஋ͱରԠ͢ΔΠϯσοΫεΛ֫ಘ͢Δ͜ͱͰ༧ଌ඼࣭είΞ ŷdim Λ֫ಘ͢Δɽŷdim = arg max Ŷdim
(4)
ydim

4.4

Attention-based LSTM

LSTM ͷಋೖʹΑͬͯൃ࿩ͷ࣌‫޲ํྻܥ‬ͷ৘ใΛଊ͑ΒΕΔΑ͏ʹͳͬͨɽ͔͠͠ɼٞ࿦ηάϝϯτதͷൃ࿩ʹ͸ٞ࿦ͷ಺༰ʹରͯ͋͠·ΓॏཁͰ͸ͳ͍ൃ࿩ɼྫ͑͹૬௤ɼͳͲ͕‫·ؚ‬Ε͍ͯΔՄೳੑ͕͋Δɽͦ͜Ͱɼ
LSTM ʹ஫ҙ‫ߏػ‬Λಋೖͨ͠ઌߦ‫[ ڀݚ‬Wang 16][Zhou 16]Λࢀߟʹɼ஫ҙ‫ ͖෇ߏػ‬LSTM ʹΑΔ඼࣭ධՁϞσϧΛ 2 छྨఏҊ͢Δɽ
·ͣɼLSTM Λ༻͍ͨ඼࣭ධՁϞσϧͱಉ༷ʹɼ࣌ࠁ i ͷ LSTM ͷঢ়ଶΛࢉग़͢Δɽͦͷ‫ޙ‬ɼLSTM ͷ֤࣌ࠁʹ͓͚Δग़ྗ hi ʹର͢ΔॏΈ ai Λ࣍ࣜΛ༻͍ͯ‫͢ࢉܭ‬ΔɽSVM࠷΋γϯϓϧͳ඼࣭ධՁϞσϧͱͯ͠ɼαϙʔτϕΫλʔϚγϯʢSVMʣ[Vapnik 13] Λ༻͍ͨϞσϧΛ
4 http://www.pluto.ai.kyutech.ac.jp/˜shimada/resources.html

ٕ 118 ٕmi = ω T tanh(hi )(5)exp(mi )ai = Nexp(mj )(6)

j=1

ω T ͸ֶशύϥϝʔλɼexp() ͸ࢦ਺ؔ਺Ͱ͋Δɽͦͷ‫ޙ‬ɼai ʹΑͬͯॏΈ͚ͮ͞Ε֤ͨ࣌ࠁʹ͓͚Δ LSTMͷӅΕ૚ hi ͷ࿨Λ༻͍ͯɼ࠷ऴతͳঢ়ଶ h∗ Λ‫ࢉܭ‬͢Δɽr=N
i ൪໨ͷൃ࿩ͷ࣌ࠁ j ʹ͓͚Δൃ࿩ LSTM ͷӅΕ૚ɼttrcU͸ i ൪໨ͷൃ࿩ͷ࣌ࠁ j ʹ͓͚Δൃ࿩ LSTM ͷi,jϝϞϦηϧɼMi ͸ i ൪໨ͷൃ࿩ͷ୯‫਺ޠ‬ΛͦΕͧΕද͢ɽͦͯ͠ɼຊϞσϧʹ͓͚Δൃ࿩ϕΫτϧ ui ͸ҎԼͷΑ͏ͳ‫Ͱܗ‬ද͞ΕΔɽ

ai hi(7)ui = [spi ; wi ; bi ; f i ; ai ]h∗ = tanh(r)(8)͜ΕҎ߱ɼ༧ଌ඼࣭είΞͷਪఆ஋ࢉग़·Ͱͷॲཧ͸
LSTM Λ༻͍ͨख๏ͱಉ༷ʹ U Λೖྗͯ͠ɼ࠷ऴతͳΛ֫ಘ͢Δɽ
ঢ়ଶ hCont
Ni=1tanh() ͸૒‫ۂ‬ઢਖ਼઀ؔ਺Ͱ͋Δɽͦͯ͠ɼ͜ͷ h∗ Λٞ࿦ηάϝϯτͷ෼ࢄද‫͠ͳݟͱݱ‬ɼग़ྗϥϕϧͷ༧ଌ֬཰෼෍ Ŷdim Λ‫ٻ‬ΊΔɽŶdim = softmax(W s h∗ + bs )(9)࠷‫ʹޙ‬ɼ෼෍ͷ࠷େ஋ͱରԠ͢ΔΠϯσοΫεΛ֫ಘ
͢Δ͜ͱͰ༧ଌ඼࣭είΞ ŷdim Λ֫ಘ͢Δɽ஫ҙ‫ߏػ‬ͷग़ྗͷΈΛ༻͍ͯ඼࣭είΞΛ༧ଌ͢ΔͱɼϊΠζΛܰ‫͖Ͱݮ‬Δ‫ظ‬଴͕͋ΔҰํɼਪ࿦ʹඞཁͳ৘ใΛܽམͯ͠͠·͏Մೳੑ΋͋Δɽͦ͜Ͱɼ஫ҙ‫ʹߏػ‬ΑΔग़ྗͱ LSTM ͷ࠷ऴঢ়ଶΛಉ࣌ʹߟྀ͢ΔϞσϧ΋ߏங͢Δɽ‫ࣜܗ‬తʹ͸ɼࣜʢ8ʣͰද‫͞ݱ‬ΕΔ࠷ऴঢ়ଶΛҎԼͷΑ͏ʹɼ஫ҙ‫ʹߏػ‬ΑΔ֤ग़ྗ૚ͷॏΈ෇͖࿨ r ͱ LSTM ͷ࠷ऴঢ়ଶ hN Λಉ࣌ʹ༻͍Δ͜ͱͰ࣮‫͞ݱ‬ΕΔɽh∗ = tanh(W r r + W hN hN )(10)

W r , W hN ͸ͦΕͧΕֶशύϥϝʔλͰ͋Δɽ‫ޙ‬͸ಉ༷ʹɼ࠷ऴঢ়ଶ h∗ Λ༻͍ͯ༧ଌ඼࣭είΞ ŷdim Λ֫ಘ͢ΔɽConthCont= LST M Cont (ui , hContii−1 , ci−1 )֊૚‫ ܕ‬LSTM

LSTM ΍஫ҙ‫ߏػ‬ͷಋೖʹΑͬͯɼٞ࿦ηάϝϯτͷൃ࿩‫ྻܥ‬ͷঢ়ଶΛߟྀͨ͠඼࣭ධՁϞσϧ͕ߏஙͰ͖ͨɽ͔͠͠ɼ‫ࡏݱ‬ͷൃ࿩ϕΫτϧͰ༻͍ΒΕ͍ͯΔti ͸୯‫ޠ‬ͷ‫ྻܥ‬৘ใΛߟྀ͍ͯ͠ͳ͍໰୊͕͋Δɽͦ͜Ͱɼൃ࿩‫ͱྻܥ‬୯‫͏͍ͱྻܥޠ‬ҟͳΔϨΠϠʔͷ‫ܥ‬ྻ৘ใΛಠཱͷ LSTM ʹΑͬͯऔΓѻ͏඼࣭ධՁϞσϧΛର࿩σʔλΛऔΓѻ͏‫[ ڀݚ‬Tran 17] Λࢀߟʹߏங͢Δɽ୯‫ॱޠ‬Λߟྀͨ͠ൃ࿩ͷςΩετ৘ใΛද‫͢ݱ‬ΔϕΫτϧ wi ͸ҎԼͷΑ͏ʹ‫͞ࢉܭ‬ΕΔɽttr
ttr
U ttr
hU= LST M U ttr (wi,j , hUi,ji,j−1 , ci,j−1 )ttr
w i = hUi,Mi(11)(12)

LST M U ttr () ͸୯‫ޠ‬ϕΫτϧͷ‫ྻܥ‬ΛΤϯίʔυ͢Δ
ൃ࿩ LSTMɼwi,j ͸ٞ࿦ηάϝϯτ S ʹ͓͚Δ i ൪໨ttr
͸ͷൃ࿩ͷ j ൪໨ͷ୯‫ޠ‬Λද‫͢ݱ‬ΔϕΫτϧɼhUi,j(14)

LST M Cont () ͸୯‫ޠ‬ϕΫτϧͷ‫ྻܥ‬ΛΤϯίʔυ͢Δ͸࣌ࠁ i ʹ͓͚Δจ຺ LSTM ͷӅൃ࿩ LSTMɼhContiCont͸࣌ࠁ i ʹ͓͚Δจ຺ LSTM ͷϝϞϦηΕ૚ɼciϧΛͦΕͧΕද͢ɽͦͯ͠ɼhN Λ༻͍ͯग़ྗϥϕϧͷ༧ଌ֬཰෼෍ Ŷdim Λ‫ٻ‬ΊΔɽŶdim = softmax(W s hCont+ bs )N(15)࠷‫ʹޙ‬ɼ෼෍ͷ࠷େ஋ͱରԠ͢ΔΠϯσοΫεΛ֫ಘ͢Δ͜ͱͰ༧ଌ඼࣭είΞ ŷdim Λ֫ಘ͢Δɽ࣮‫ݧ‬5ຊઅͰ͸ɼ4 અͰఏҊͨ͠ख๏Λຊ‫ߏͰڀݚ‬ஙͨ͠σʔληοτʹద༻ͨ݁͠Ռʹ͍ͭͯ·ͱΊΔɽ

5.1
4.5(13)࣮‫ݧ‬ઃఆຊ‫Ͱڀݚ‬͸ɼൃ࿩ऀͷ੾ΓସΘΓΛද͢ม਺ spi ͸ɼ࣌ࠁ i ͷൃ࿩ͷ࿩ऀͱ࣌ࠁ i − 1 ͷൃ࿩ͷ࿩ऀ͕Ұக͠
͍ͯΔ৔߹͸ 0 ͱ͠ɼͦΕҎ֎͸ 1 ͱ͍ͯ͠ΔɽϞσϧͷೖྗͱͳΔൃ࿩ͷςΩετϕΫτϧ ti ͓Αͼ wi,jʹ͍ͭͯ͸ɼ౦๺େֶ͕ެ։͍ͯ͠Δ BERT ͷࣄલֶ
शϞσϧ5 Λ༻͍ͯ֫ಘ͢Δɽຊ‫Ͱڀݚ‬͸ [CLS] τʔΫϯʹରԠ͢Δ BERT ͷ 11 ૚໨Λ ti ɼ֤୯‫ʹޠ‬ରԠ͢
Δ BERT ͷ 11 ૚໨Λ wi,j ͱͨ͠ɽൃ࿩ͷಈ࡞৘ใΛද͢ϕΫτϧ bi ʹ͍ͭͯ͸ɼOpenPose ʹΑͬͯਪఆ͞Εͨൃ࿩தͷ্൒਎ͷ xy ࠲ඪ఺ΛͦΕͧΕϑϨʔϜͷਐߦํ޲ʹରͯ͠ฏ‫ۉ‬஋͓Αͼඪ४ภࠩͰ৞ΈࠐΜͩϕΫτϧͱͨ͠ɽൃ࿩ͷද৘৘ใΛද͢ϕΫτϧ f iʹ͍ͭͯ͸ɼOpenFace ʹΑͬͯਪఆ͞Εͨ‫͓إ‬Αͼ໨ͷಛ௃఺ͷ xy ࠲ඪɼࢹઢํ޲ɼ಄ͷҐஔͱ޲͖ɼ͓Αͼ Facial Action Units (AUs) ͷ༗ແͷ஋ΛͦΕͧΕϑϨʔϜͷਐߦํ޲ʹରͯ͠ฏ‫ۉ‬஋͓Αͼඪ४ภࠩͰ৞ΈࠐΜͩϕΫτϧͱͨ͠ɽൃ࿩ͷԻ੠৘ใΛද͢ϕٕ 119 ٕ5 https://github.com/cl-tohoku/bert-japaneseද 3: ࣮‫͍༻Ͱݧ‬Δਖ਼ղείΞͷ෼෍ධՁ࣠

LMHRe
Ef13
989
977672

Ϋτϧ ai ʹ͍ͭͯ͸ɼSurfboard ʹΑͬͯ֫ಘͨ͠ൃ
࿩Ի੠ͷ 13 ࣍‫ ݩ‬MFCCɼRMSɼ‫ج‬ຊप೾਺ɼεϖΫτϧॏ৺ͷ஋ΛͦΕͧΕ࣌ؒͷਐߦํ޲ʹରͯ͠࠷খ஋ɼ࠷େ஋ɼฏ‫ۉ‬஋ɼ͓Αͼඪ४ภࠩͰ৞ΈࠐΜͩ஋ͱɼδολͱγϚͷ஋Λ༗͢ΔϕΫτϧͱͨ͠ɽ13 ࣍‫ ݩ‬MFCC ͸ൃ࿩࣌ͷ‫ޱ‬΍޴ͷ‫ܗ‬Λද‫͢ݱ‬Δ੠ಓಛੑɼRMS ͸੠ͷେ͖͞ɼ‫ج‬ຊप೾਺͸੠ͷߴ͞ɼεϖΫτϧॏ৺͸੠ͷ໌Δ͞ɼδολ͸੠ͷߴ͞ͷΏΕɼγϚ͸੠ͷେ͖͞ͷΏΕͱͦΕͧΕରԠ͍ͯ͠Δɽຊ‫Ͱڀݚ‬͸Ћ܎਺͕૬ରతʹߴ͍ओཁධՁ࣠ͷ Re ͱEf Λਖ਼ղϥϕϧʹ༻͍࣮ͯ‫ݧ‬Λ࣮ࢪ͢Δɽߋʹɼϥϕϧ͋ͨΓͷσʔλ਺֬อͷͨΊʹɼओཁධՁ࣠ͷείΞ VL ͓Αͼ VH ΛͦΕͧΕ LɼH ͱ‫͢ͳݟ‬ϏχϯάॲཧΛߦ͍ɼσʔληοτͷ࠶ߏஙΛߦͬͨɽ࣮‫ʹݧ‬༻͍Δσʔληοτͷ౷‫ܭ‬஋Λද 3 ʹࣔ͢ɽຊ‫͚͓ʹڀݚ‬Δ඼࣭ਪఆλεΫ͸ LɼMɼH ͷ 3 ͭͷλάΛ෇༩͢Δ 3 ஋෼ྨ໰୊ͱͯ͠ఆࣜԽͰ͖Δɽ͠
͕ͨͬͯɼਂ૚ֶशͷϞσϧͷଛࣦ͸ L2 ਖ਼ଇԽ෇ΫϩεΤϯτϩϐʔ‫ࠩޡ‬Λ༻͍ɼ͜ͷଛࣦΛ࠷খʹ͢Δํ޲ͰֶशΛ࣮ࢪͨ͠ɽ࠷దԽख๏͸ SGD [Bottou 91]ʹ‫׳‬ੑ߲ʢMomentumʣ [Qian 99] Λ෇༩ͨ͠ SGDMomentumʢ‫׳‬ੑ߲ α = 0.95ʣΛར༻ͨ͠ɽϋΠύʔύϥϝʔλʹ͍ͭͯ͸ɼӅΕ૚Λ 500 ࣍‫ݩ‬ɼόοναΠζΛ 32ɼΤϙοΫ਺Λ 50ɼֶश཰Λ 0.01ɼυϩοϓΞ΢τΛ 0.2ɼॏΈ‫ݮ‬ਰΛ 0.001 ʹઃఆͨ͠ɽຊ‫ߏͰڀݚ‬ஙͨ͠ίʔύεͷऩ࿥ର࿩਺͸ 10 ର࿩ɼٞ࿦ηάϝϯτ਺͕ 178 ͱগ਺Ͱ͋Δɽͦ͜Ͱɼ‫܇‬࿅σʔλ 8 ର࿩ɼ‫ূݕ‬σʔλ 1 ର࿩ɼධՁσʔλ 1 ର࿩ͱ͍͏‫ Ͱܗ‬10 ର࿩ަࠩ‫ূݕ‬Λ࣮ࢪͨ݁͠ՌΛ࣮‫ݧ‬ͷධՁ஋ͱ͢ΔɽධՁࢦඪʹ͸֤ϥϕϧʹ͍ͭͯ F ஋Λࢉग़͠ɼͦͷϚΠΫϩฏ‫ۉ‬Λར༻ͨ͠ɽ·ͨɼ݁Ռͷ‫ؤ‬݈ੑΛอূ͢ΔͨΊʹ࣮‫ݧ‬Λ 5 ճ࣮ࢪ͠ɼຊ࿦จͰ͸ͦͷϚΫϩฏ‫ۉ‬Λใࠂɼٞ࿦͢Δɽ

5.2࣮‫݁ݧ‬Ռ

ද 4 ʹɼ֤ϞμϦςΟͷ૊Έ߹ΘͤʹΑΔධՁੑೳΛࣔ͢ɽbaseline ͸શͯͷηάϝϯτͷ඼࣭Λ M Λ༧ଌͨ͠৔߹ͷධՁ஋Ͱ͋Γɼଠࣈ͸֤ϞμϦςΟͷ૊Έ߹Θͤʹ͓͍ͯͷ࠷ߴ஋Λࣔ͢ɽRe ʹ͍ͭͯ͸ɼϞσϧ΁ͱೖྗ͢ΔϞμϦςΟͷ৘ใΛ֦ுͯ͠΋ɼSVM ͓Αͼਂ૚ֶशͷϞσϧʹ͓͍ͯ඼࣭ਪఆੑೳͷେ͖ͳมԽ͸‫ݟ‬ΒΕͳ͔ͬͨɽϢχϞʔμϧɼόΠϞʔμϧɼϚϧνϞʔμϧͷઃఆʹ͓͚Δ࠷ߴੑೳϞσϧʢଠࣈ+ԼઢʣΛൺֱ͢Δͱɼ‫ޠݴ‬৘ใͱ‫إ‬৘ใΛ༻͍ͨ HLSTM ͕࠷େੑೳʢ0.459ʣͱͳͬͨɽ͔͠͠ɼ͜ͷධՁ஋͸‫ޠݴ‬৘ใΛ༻͍ͨ SVMͷධՁ஋ʢ0.451ʣͱ 0.008 ͔͠มΘΒͣɼͦΕҎ֎ͷϞσϧʹ͍ͭͯ͸είΞ͕௿͘ͳ͍ͬͯΔɽͭ·Γɼ߹ཧੑʢReʣʹؔ͢ΔධՁʹ͓͍ͯ͸‫ʹ֎ޠݴ‬ΑΔ৘ใ͸༗ޮͰ͸ͳ͍Մೳੑ͕͋Δͱߟ͑ΒΕΔɽҰํɼEfʹ͍ͭͯͷ݁ՌΛ֬ೝ͢ΔͱɼSVM Ҏ֎ʹ͍ͭͯ͸ೖྗϞμϦςΟͷछྨ͕૿Ճ͢Δ͝ͱʹ඼࣭ਪఆͷੑೳ͕૿Ճ͢Δ܏޲͕͍͔ͭ͘‫ݟ‬ΒΕΔ͜ͱΛ֬ೝͨ͠ɽಛʹɼશͯͷϞμϦςΟΛ༻͍ͨ A-LSTM1 ͕࠷΋ߴ͍ධՁ஋ʢ0.490ʣͱͳͬͨɽͭ·ΓɼEf ʹؔ͢ΔධՁʹ͓͍ͯ͸ɼ‫ޠݴ‬৘ใͷΈͳΒͣɼͦͷൃ‫ऀݴ‬ͷಈ࡞΍ද৘ɼ੠ͷτʔϯͳͲ༷ʑͳ৘ใΛ༻͍Δ͜ͱ͕ॏཁͰ͋Δͱߟ͑ΒΕΔɽ͜ͷ࣮‫݁ݧ‬Ռʹ͍ͭͯ͸ɼRe ͱ Ef ͦΕͧΕͷఆٛͱൺֱ͢ΔͱҰ؏͍ͯ͠Δ͜ͱ͕Θ͔Δɽͭ·Γɼ߹ཧੑʢReʣ͸ٞ࿦ͷ༰ೝੑɼؔ࿈ੑɼॆ଍ੑͱ͍ͬͨٞ࿦ͷ಺༰ͦͷ΋ͷʹؔ࿈͢ΔධՁ࣠Ͱ͋Γɼ‫ޠݴ‬తͳ಺༰Ҏ֎ͷཁҼͰ͸‫ج‬ຊతʹධఆ஋͕લ‫͍ͳ͠ޙ‬΋ͷͰ͋ΔɽͦͷͨΊɼ֤Ϟσϧʹ‫ޠݴ‬Ҏ֎ͷϞμϦςΟ৘ใΛಋೖͯ͠΋߹ཧੑʹؔ͢Δ඼࣭ධՁͷੑೳ͸޲্͠ͳ͍ͱߟ͑ΒΕΔɽҰํɼ༗ޮੑʢEfʣ͸ٞ࿦ͷ৴༻ੑ΍৘ಈੑͱ͍ͬͨௌऺଆͷ‫ײ‬৘΍ɼ໌ྎੑɼଥ౰ੑɼॱংੑͱ͍ͬͨٞ࿦ͷཧղ͠΍͢͞Λද͢ධఆ࣠Ͱ͋ΔɽҰൠʹɼਓʹ৴༻ͯ͠΋Β͑ΔΑ͏ʹ෺ࣄΛ఻͑ͨΓɼਓʹ‫Ͳͳײڞ‬ͷ‫ײ‬৘Λ‫ٻ‬ΊͨΓ͢Δ৔߹ʹ͸ΞΠίϯλΫτ΍ϘσΟϥϯήʔδͳͲ‫Ͱ֎ݴ‬Կ͔޻෉Λߦ͏͜ͱ͕ߟ͑ΒΕΔɽͦͷͨΊɼ༗ޮੑͷ඼࣭ධՁͷੑೳ͸‫ޠݴ‬ɾඇ‫ޠݴ‬ͷ྆ํΛಋೖ͢Δ͜ͱͰ޲্͢ΔͱਪଌͰ͖Δɽ

6͓ΘΓʹຊ‫Ͱڀݚ‬͸ෳ਺ਓٞ࿦ͷࣗಈ඼࣭ධՁΛ໨తͱͯ͠ɼෳ਺ਓٞ࿦ίʔύεͱ඼࣭ϥϕϧͷϖΞσʔληοτΛ࡞੒͠ɼͦͷσʔλΛର৅ͱͨ͠ͱٞ࿦ͷࣗಈධՁϞσϧΛߏஙɼੑೳධՁΛ࣮ࢪͨ͠ɽຊ‫Ͱڀݚ‬͸৽‫ܕ‬ίϩφ΢Πϧεͷ‫ײ‬છ֦େΛड͚ɼे෼ͳର࿩਺Λऩ࿥ͨ͠ίʔύεͷߏஙΛ࣮‫͢ݱ‬Δ͜ͱ͕Ͱ͖͍ͯͳ͍ɽͦͷͨΊࠓ‫ޙ‬͸ɼߋͳΔ਺ͷٞ࿦ΛίʔύεԽ͠ɼσʔλΛ֦ॆ͢Δ͜ͱ͕٤‫ۓ‬ͷ՝୊ͱͯ͋͛͠ΒΕΔɽ·ͨɼ࣭ͷߴ͍ਖ਼ղϥϕϧΛߏங͢Δख๏ཱ͕֬͞Ε͍ͯͳ͍ͨΊɼνΣοΫϦετํࣜΛಋೖ΍ධՁ࣠ͷఆٛΛ؆қԽ͢ΔͳͲͷ޻෉Λߦ͍ɼΑΓ৴པੑͷߴ͍඼࣭ϥϕϧΛ෇༩͢Δखଓ͖ͷߏங͕ඞཁͰ͋Δɽຊ
‫Ͱڀݚ‬͸࣮‫߹Ͱݧ‬ཧੑͱ༗ޮੑͦΕͧΕ F ஋Ͱ 0.459ɼ

ٕ 120 ٕද 4: ֤ϞμϦςΟͷ૊Έ߹ΘͤʹΑΔ֤ϞσϧͷධՁੑೳTTBධՁੑೳʢϚΠΫϩฏ‫ۉ‬ͷฏ‫ۉ‬஋ʣ
TF
TATBFTBATFA

TBFA

Re

baseline
SVM
LSTM
A-LSTM1
A-LSTM2
HLSTM

0.333
0.451
0.387
0.412
0.371
0.359

0.333
0.338
0.398
0.392
0.337
0.354

0.333
0.337
0.392
0.387
0.400
0.459

0.333
0.343
0.380
0.399
0.420
0.388

0.333
0.333
0.410
0.371
0.404
0.415

0.333
0.317
0.379
0.359
0.374
0.391

0.333
0.320
0.388
0.398
0.387
0.370

0.333
0.340
0.360
0.398
0.339
0.405

Ef

baseline
SVM
LSTM
A-LSTM1
A-LSTM2
HLSTM

0.384
0.459
0.428
0.433
0.452
0.459

0.384
0.382
0.478
0.470
0.412
0.433

0.384
0.383
0.438
0.426
0.416
0.416

0.384
0.436
0.476
0.468
0.436
0.379

0.384
0.384
0.467
0.450
0.443
0.414

0.384
0.392
0.472
0.396
0.425
0.440

0.384
0.406
0.486
0.444
0.401
0.431

0.384
0.379
0.435
0.490
0.404
0.451

ධՁ࣠

Ϟσϧ

0.490 ఔ౓ͷਫ਼౓Ͱ඼࣭ਪఆ͕ՄೳͰ͋Δ͜ͱΛ͕ࣔͨ͠ɼ͜ΕΒͷείΞʹ͸վળͷ༨஍͕͋ΔɽߋͳΔਫ਼౓޲্Λ໨ࢦ͠਺ཧٞ࿦ֶ [ए໦ 17] ͷ஌‫ݟ‬΍஌ࣝάϥϑ [Al-Khatib 20] ͳͲͷੈք஌ࣝΛ༻͍ͨධՁϞσϧͷվྑΛߦ͍ͬͯ͘ඞཁ͕͋Δɽ

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Long Short-Term Memory Networks for Relation Classiﬁcation, in Proceedings of ACL, pp. 207–212 (2016)[ए໦ 17] ए໦ རࢠ, ৽ా ࠀ‫ݾ‬ɿ਺ཧٞ࿦ֶ, ౦‫ػిژ‬େֶग़൛‫( ہ‬2017)[෢઒ 18] ෢઒ ௚थ ଞɿάϧʔϓσΟεΧογϣϯʹ͓͚Δൃ‫ऀݴ‬ͷ‫ޠݴ‬/ඇ‫ޠݴ‬ͷදग़ͱධՁऀධՁͷؔ܎ͷ෼ੳ,ిࢠ৘ใ௨৴ֶձ࿦จࢽ D, Vol. 101, No. 2, pp. 284–293(2018)[ྛ 15] ྛ ༎थ ଞɿάϧʔϓσΟεΧογϣϯίʔύεͷߏங͓Αͼੑ֨ಛੑͱͷؔ࿈ੑͷ෼ੳ, ৘ใॲཧֶձ࿦จࢽ, Vol. 56, No. 4, pp. 1217–1227 (2015)

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