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/
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
n ࣄྫʹͱͮ͘ϊϯύϥϝτϦοΫΞϓϩʔνΛ࠾༻ n ྨࣅࣄྫ͔ΒਪϧʔϧΛऔಘͯ͠ར༻ n طଘख๏ͷ n ΤϯςΟςΟؒͷਪϧʔϧΛϞσϧԽ n ྫ: ceo(X, Y)⋀ headquatered(Y, Z) ⟹ works_in_city(X, Z) n ͯ͢ͷਪϧʔϧΛϞσϧύϥϝʔλʹຒΊࠐΉͷࠔ
∧ 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”
ྨ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
n 2ͭͷϕϯνϚʔΫσʔληοτͰ࠷ߴਫ਼Λୡ n ఏҊख๏ʹΑͬͯɼਖ਼ղʹḷΓͭ͘ଟ༷ͳਪύεΛ ൃݟͰ͖Δ͜ͱ͕Θ͔ͬͨ n ࣄྫʹͱͮ͘ਪ໘ന͍ n ະͷ(ςετσʔλ)Λղ͘ͱ͖ʹɼֶशσʔλ Λࢀর͠ͳ͕Βղ͘ͱ͍͍͜ͱ͕͋Δ ·ͱΊ Take-Home Message ஶऀʹΑΔ࣍ճ࡞