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TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20280312T030000 RDATE:20281105T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20290311T030000 RDATE:20291104T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20300310T030000 RDATE:20301103T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20310309T030000 RDATE:20311102T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20320314T030000 RDATE:20321107T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20330313T030000 RDATE:20331106T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20340312T030000 RDATE:20341105T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:9968055d884580e95e8a56ef5551c627 CATEGORIES:Qualifying Exam CREATED:20190823T084023 SUMMARY:Towards Selective Composition of Facts for Generating Open-domain Entity Descriptions LOCATION:Hill 482 DESCRIPTION;ENCODING=QUOTED-PRINTABLE:
Abstract :
Select
ive compositionality – the ability to select relevant concepts from the acq
uired knowledge and composing them into a coherent
linguistic form – i
s one of the cornerstones of human language. In this work, we study a speci
fic form of selective compositionality where the
goal is to synthesize
a succinct textual description of an entity given the available factual kn
owledge. Large-scale open-domain knowledge
graphs such as Wikidata and
Google Knowledge Graph sparsely provide a synoptic textual description tha
t entails the most discerning facts of an entity (e.g. Roger Federer: Swiss
tennis player). Such descriptions effectively provide for a near-instantan
eous human understanding of an entity and are also useful in a number of li
nguistic tasks, including named entity disambiguation. Additionally, they c
an serve as
fine-grained ontological types in question answering and r
easoning-driven applications. Unfortunately, due to the rapid increase in t
he number of entities, such descriptions are missing for many entities in t
he existing knowledge graphs, necessitating an automated
synthesis of
succinct textual descriptions from underlying factual knowledge. To this en
d, we propose a neural architecture that can
precisely generate synopt
ic descriptions for a diverse set of entities. Through extensive evaluation
, we demonstrate the ability of our
architecture to select relevant fa
cts and composing them to coherent and succinct descriptions more accuratel
y by pitting it against a number of competitive baselines.