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A Simple Approach to Case-Based Reasoning in Knowledge Bases
2019-09-25 第12回最先端NLP勉強会
Hiroki Ouchi
September 23, 2020
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23 slides
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2020-09-25 @ ୈ12ճ࠷ઌ NLP ษڧձ ಡΉਓɿେܒथ ʢཧݚʣ ※ͱ͘ʹऍ͕ͳ͍ݶΓਤදจ͔ΒͷҾ༻Ͱ͢ Paper: https://openreview.net/pdf?id=AEY9tRqlU7
Code: https://github.com/rajarshd/CBR-AKBC -
2020/9/18 ͜ͷจΛબΜͩཧ༝ n ࣄྫ (Case/Example/Instance) ʹͱͮ͘ਪʹڵຯ Ø ࣗࣅͨݚڀΛ͓ͯ͠Γɼ͜ͷํੑͷએΛ͍ͯ͠Δ ”Instance-Based
Learning of Span Representations,” Ouchi+’20 Ø ͜ͷํੑ͕ࠓޙ૿͍͔͑ͯ͘ ”All the reviewers think this is a strong paper and would lay out a solid framework for future work in this direction.” (Meta-Reviewer, OpenReview) n Best Paper Runner-Up (@AKBC) ʹબग़ Ø ϨϏϡʔ(10ຬ): 8, 8, 7 From http://www.akbc.ws/2020/awards/ -
2020/9/25 Take-Home Message n ࣄྫʹͱͮ͘ਪ໘ന͍ n ະͷ(ςετσʔλ)Λղ͘ͱ͖ʹɼֶशσʔλ Λࢀর͠ͳ͕Βղ͘ͱ͍͍͜ͱ͕͋Δ n
͍ΖΜͳλεΫʹԠ༻Մೳ n ࣄྫʹͱͮ͘ਪͷೖॻ -
2020/9/25 ຊจͷ·ͱΊ n ࣄྫʹͱ͍ͮͯਪ (Case-Based Reasoning) ͢Δ γϯϓϧͳख๏ΛఏҊ n
Ϟσϧύϥϝʔλͷֶश͕͍Βͳ͍ n 2ͭͷϕϯνϚʔΫσʔληοτ(NELL-995ͱFB-122) Ͱ࠷ߴਫ਼Λୡ n ఏҊख๏ʹΑͬͯɼਖ਼ղʹḷΓͭ͘ଟ༷ͳਪύεΛ ൃݟͰ͖Δ͜ͱ͕Θ͔ͬͨ -
2020/9/25 Case-Based Reasoning (CBR) ͱʁ n աڈʹܦݧͨ͠ྨࣅͷղ๏ʹج͍ͮͯ৽ͨͳ Λղ͘ਪख๏·ͨͦͷաఔ [Aamodt
and Plaza, 1994] n ྫʣࣗಈंඋ࢜ɼҎલʹܦݧͨ͜͠ͱͷ͋ΔࣅͨΑ͏ͳ ंͷނোΛࢥ͍ग़ͯ͠ɼंͷमཧΛ͢Δ n ͜Ε·Ͱଟ͘ͷݚڀ͕ೝ৺ཧֶͰͳ͞Ε͖ͯͨ n ଟ͘ͷݹయతͳਓೳͷݚڀͰ CBR Λ AI γεςϜʹ औΓೖΕΔ͜ͱʹྗ͖ͯͨ͠ [Schank, 1982, Kolodner, 1983, Rissland, 1983, Aamodt and Plaza, 1994, Leake, 1996] n ͓͓·͔ͳΠϝʔδ n Ұൠతͳྨثϕʔεͷख๏ʮ࣋ͪࠐΈෆՄࢼݧʯ Ø ͋Β͔͡Ίֶशσʔλ͔ΒඞཁͳࣝΛهԱ͠ɼ هԱͨࣝ͠Λ༻͍ͯະͷΛղ͘ n CBR ͳͲͷࣄྫϕʔεͷख๏ʮ࣋ͪࠐΈՄೳࢼݧʯ Ø ֶशσʔλΛࢀর͠ͳ͕ΒະͷΛղ͘ -
2020/9/25 Case-Based Reasoning (CBR) ͱʁ n CBR 4ͭͷεςοϓ͔Βߏ͞ΕΔ [Aamodt
and Plaza, 1994] ᶃ Retrieve: ະͷ͕༩͑ΒΕͨͱ͖ɼͦΕͱྨࣅ͢Δ ࣄྫΛݕࡧɽ1ͭͷࣄྫͱͦͷղ๏͔ΒͳΔͱ͞ΕΔ ᶄ Reuse: ݕࡧͨ͠ࣄྫͷղ๏Λ༩͑ΒΕͨద༻ ᶅ Revise: ᶄͷղ๏͕ద༻Ͱ͖ͳ͍߹ɼղ๏Λվྑɾमਖ਼ ᶆ Retain: ͕ղ͚ͨΒɼͦͷࣄྫΛ৽ͨͳܦݧͱͯ͠อ࣋ ຊݚڀͷఏҊख๏͜ͷ4εςοϓʹґڌ -
2020/9/24 औΓΉλεΫ n Query Answering (Link Prediction) n ೖྗ:
ΫΤϦ(eq , rq , ?) n ͜͜Ͱ eq ΤϯςΟςΟɼrq ؔΛද͢ n ग़ྗ: ? ʹ͋ͯ·ΔΤϯςΟςΟ rq eq -
2020/9/25 ͳͥ CBR Λ࠾༻͢Δͷ͔ʁ n طଘख๏ͷ n ΤϯςΟςΟؒͷਪϧʔϧΛϞσϧԽ n
ྫ: ceo(X, Y)⋀ headquatered(Y, Z) ⟹ works_in_city(X, Z) n ͯ͢ͷਪϧʔϧΛϞσϧύϥϝʔλʹຒΊࠐΉͷࠔ ਐΉํϞσϧ ͷग़ྗ͢ΔείΞ Ͱຖ࣌ࠁܾΊΔ -
2020/9/25 ͳͥ CBR Λ࠾༻͢Δͷ͔ʁ ਐΉํϞσϧ ͷग़ྗ͢ΔείΞ Ͱຖ࣌ࠁܾΊΔ n ఏҊख๏ʹΑΔͷ؇ࡦ
n ࣄྫʹͱͮ͘ϊϯύϥϝτϦοΫΞϓϩʔνΛ࠾༻ n ྨࣅࣄྫ͔ΒਪϧʔϧΛऔಘͯ͠ར༻ n طଘख๏ͷ n ΤϯςΟςΟؒͷਪϧʔϧΛϞσϧԽ n ྫ: ceo(X, Y)⋀ headquatered(Y, Z) ⟹ works_in_city(X, Z) n ͯ͢ͷਪϧʔϧΛϞσϧύϥϝʔλʹຒΊࠐΉͷࠔ -
2020/9/24 εςοϓᶃ ྨࣅΤϯςΟςΟͷݕࡧ n ΫΤϦ eq ͱྨࣅͷΤϯςΟςΟ e’ Λݕࡧ
n ͨͩ͠ e’ ؔ rq ͷ outgoing edge Λ࣋ͭ eq e’ ※ΤϯςΟςΟؒྨࣅͷܭࢉํ๏ Section 2.2 Λࢀর -
εςοϓᶄ ਪύεͷऔಘ n ྨࣅΤϯςΟςΟ e’ ͱؔ rq ͷΤϯςΟςΟ e’’
Λ ͭͳ͙ਪύεΛऔಘ rq rq e’ e’ e’’ e’’ 2020/9/21 -
εςοϓᶅ ਪύεΛద༻ n औಘͨ͠ਪύεΛΫΤϦ (eq , rq , ?)
ʹద༻ n ਪύεద༻ͷࡍ໊ؔͷจࣈྻશҰகΛ࠾༻ works_in_city(x,z) ⟸ ceo(x,y)∧ headquatered(y,z) MATCH MATCH औಘͨ͠ਪύε e’’ eq rq -
2020/9/21 εςοϓᶆ ਪύεͷอଘ n ͏·͘ద༻Ͱ͖ͨ(ΫΤϦΤϯςΟςΟ eq ͱؔ rq ͷ
ΤϯςΟςΟ e’’ ʹḷΓ͍ͭͨ)ਪύεΛอଘ -
2020/9/23 ӅΕεςοϓᶇ ͑ͷϥϯΩϯά n ਪύεΛͬͯḷΓ͍ͭͨΤϯςΟςΟΛΧϯτ • works_in_city(x,z) ⟸ ceo(x,y)
∧ headquatered(y,z) Ø z = Seattle Ø Seattle += 1 • works_in_city(x,z) ⟸ ceo(x,y) ∧ based_in(y,z) Ø z = Washington Ø Washington += 1 ɾ ɾ Seattle 7 Washington 2 U.S. 1 New York 1 n Χϯτͷଟ͍ॱʹΤϯςΟςΟΛϥϯΩϯά ※͜ͷεςοϓஶऀΒͷҎԼͷهड़Λͱʹେ͕ਪଌ ``we rank the entities based on the number of reasoning paths that lead to them” -
2020/9/24 ࣮ݧɿFew-Shot Learning ʹ͓͚Δ݁Ռ n ఏҊख๏ֶश͕ෆཁͰɼগͷྨࣅΤϯςΟςΟʹ ؔ࿈͢ΔਪύεΛ༻͍ͯ༧ଌ͢Δ Ø ֶशࣄྫ͕ݶΒΕ͍ͯΔؔ(few-shot
relations)ʹ ର͢Δੑೳ͕ྑ͍ͷͰͳ͍͔ʁ n طଘݚڀ [Lv+’19] ʹ͕͍ͨ͠ɼNELL-995 தͷස 114ະຬͷؔΛ few-shot relations ͱఆ࣮ٛͯ͠ݧ -
2020/9/24 ࣮ݧɿFew-Shot Learning ʹ͓͚Δ݁Ռ Ø ଞͷख๏ΑΓ few-shot relations ʹڧ͍
n ఏҊख๏ֶश͕ෆཁͰɼগͷྨࣅΤϯςΟςΟʹ ؔ࿈͢ΔਪύεΛ༻͍ͯ༧ଌ͢Δ Ø ֶशࣄྫ͕ݶΒΕ͍ͯΔؔ(few-shot relations)ʹ ର͢Δੑೳ͕ྑ͍ͷͰͳ͍͔ʁ -
2020/9/24 ੳɿͳͥ CBR ͷੑೳྑ͍ͷ͔ʁ n ͻͱͭͷ͕ؔଟ༷ͳ;ͨͭͷΤϯςΟςΟΛͭͳ͙ Ø ͨͱ͑ಉؔ͡ͰҟͳΔਪύεΛֶश͢Δඞཁ͕͋Δ GEORGE
BUSH HOUSE OF REPUBLICANS agent_belongs_to _organization VANCOUVER CANUCKS NHL agent_belongs_to _organization A B X Z Y -
2020/9/24 ੳɿͳͥ CBR ͷੑೳྑ͍ͷ͔ʁ n CBR ΫΤϦΤϯςΟςΟͱࣅ͍ͯΔΤϯςΟςΟΛ ݕࡧ͠ɼͦͷपลͷਪύεΛར༻͢Δ Ø
ྨࣅΤϯςΟςΟಉ࢜ͳΒɼࣅͨ(͋Δ͍ಉ͡)ਪύεΛ ͑Δ߹͕ൺֱతଟ͍ [େิ] Ø ྨࣅΤϯςΟςΟ͝ͱʹݸผͰਪ͍͢͠Έʹͳͬͯ ͍Δͣ (fine-grained contextual encoding) Ø ͜ͷ͕(ύϥϝʔλʹͯ͢ͷਪࣝΛຒΊࠐΉܥͷ) طଘϞσϧͱൺͯ༗རͳͱߟ͑ΒΕΔ GEORGE BUSH HOUSE OF REPUBLICANS agent_belongs_to _organization A B BARACK OBAMA HOUSE OF REPUBLICANS agent_belongs_to _organization A B ྨࣅ ※͜ͷεϥΠυ Section 3.4 ͷهड़ͷߦؒΛେ͕ଟʹຒΊͨͷͰ͋Δ -
2020/9/24 ੳɿͳͥ CBR ͷੑೳྑ͍ͷ͔ʁ n Ծઆɿͯ͢ͷਪࣝ(ύε)ΛϞσϧύϥϝʔλʹ ֮͑ͤ͞Δͷ͍͠ n ≒
CBR ͷΑ͏ʹɼ༧ଌʹඞཁͱͳΔਪύεΛʹԠͯ͡ దٓݕࡧͯ͠༻͍ͨํ͕൚Խ͍ͤ͢͞ n ݕূํ๏ɿਖ਼ղΤϯςΟςΟʹḷΓͭ͘(Ϟσϧ͕༧ଌ ͨ͠)ਪύεͷΛ͑ͯൺֱ͢Δ Ø ֮͑ͨਪ͕ࣝগͳ͚Εɼਖ਼ղΤϯςΟςΟʹḷΓͭ͘ ਪύεൃݟͰ͖ͳ͍ͱ͍͏લఏ͕͋Δ(ͨͿΜ) [େิ] ※͜ͷεϥΠυ Section 3.4 ͷهड़ͷߦؒΛେ͕ଟʹຒΊͨͷͰ͋Δ n ݕূ݁Ռ Ø ఏҊख๏(CBR): 306.4 unique paths Ø طଘख๏(MINERVA): 176.83 unique paths n ݁ɿԾઆࢧ࣋͞Εͨ n දతͳطଘख๏(MINERVA)ΑΓ ఏҊख๏(CBR) ͷ΄͏͕ɼ ਖ਼ղΤϯςΟςΟʹḷΓͭ͘ਪύεΛଟ͘ൃݟͰ͖Δ -
2020/9/24 ຊจͷݶք n ਪύεద༻࣌ͷ໊ؔͷจࣈྻશҰக Ø ؔͷࢄදݱΛ༻͍ͯྨࣅΛܭࢉ͢Δ͜ͱʹ Αͬͯ͜ͷΛ؇͍ͨ͠ works_in_city(x,z) ⟸
ceo(x,y)∧ headquatered(y,z) MATCH MATCH औಘͨ͠ਪύε e’’ eq rq -
2020/9/25 ຊจͷ·ͱΊ & Take-Home Message n γϯϓϧͳ CBR ͷख๏ΛఏҊ
n 2ͭͷϕϯνϚʔΫσʔληοτͰ࠷ߴਫ਼Λୡ n ఏҊख๏ʹΑͬͯɼਖ਼ղʹḷΓͭ͘ଟ༷ͳਪύεΛ ൃݟͰ͖Δ͜ͱ͕Θ͔ͬͨ n ࣄྫʹͱͮ͘ਪ໘ന͍ n ະͷ(ςετσʔλ)Λղ͘ͱ͖ʹɼֶशσʔλ Λࢀর͠ͳ͕Βղ͘ͱ͍͍͜ͱ͕͋Δ ·ͱΊ Take-Home Message ஶऀʹΑΔ࣍ճ࡞