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C. China_men's_national_basketball_team: The Chinese men's national basketball team represents the … Neural Networks EntEval: A Holistic Evaluation Benchmark for Entity Representations Mingda Chen* 1 , Zewei Chu* 2 , Yang Chen 4 , Karl Stratos 3 , Kevin Gimpel 1 1 Toyota Technological Institute at Chicago 2 University of Chicago 3 Rutgers University 4 Ohio State University Learning Entity Representations Entity Fixed-length vector We are interested in two approaches: o Contextualized entity representations (CER) that encode an entity based on the context it appears regardless of whether the entity is seen before. o Descriptive entity representations (DER) that rely on entries in Wikipedia. EntEval § 7 probing task groups. Entity Typing (ET) ET = assign types to an entity given only the mention context. Logic was established as a discipline by Aristotle, who established its fundamental place in philosophy. Wisdom University Philosophy Accident … Coreference Arc Prediction (CAP) CAP = classify if two entities are the same given context Revenues of $14.5 billion were posted by [Dell]. [The company] ... EntEval cont. Experiment Results Entity Factuality Prediction (EFP) EFP = classify the correctness of statements for entities. TD Garden has held Bruins games. Named Entity Disambiguation (NED) NED = link a named-entity mention to its entry in a knowledge base. SOCCER - JAPAN GET LUCKY WIN, CHINA IN SURPRISE DEFEAT. D. China_PR_national_football_team: The Chinese national football team recognized as China PR by FIFA … A. China: China is a country in East Asia … B. Porcelain: Porcelain is a ceramic material … *Equal Contribution. Listed in alphabetical order. Hyperlink-Based Training Given a context sentence with mention span and a description sentence We use the same bidirectional language modeling loss as in ELMo, where x 1:T x (i, j ) y 1:Ty l lang (x 1:Tx )+ l lang (y 1:Ty ) In addition, we define two bag-of-words reconstruction losses l ctx = - X t log q(x t |f ELMo ([BOD]y 1:Ty , 1,T y )) l desc = - X t log q(y t |f ELMo ([BOC]x 1:Tx , i, j )) The final training loss for EntELMo is l lang (x 1:T x )+ l lang (y 1:T y )+ l ctx + l desc Special symbols prepended to sentences to distinguish descriptions from contexts. ET CAP EFP NED CP ERT ESR GloVe 10.3 71.9 67.0 41.2 52.6 40.8 50.9 BERT Base 32.0 80.6 74.8 50.6 65.6 42.2 28.8 BERT Large 32.3 79.1 76.7 54.3 66.9 48.8 32.6 ELMo 35.6 79.1 75.8 51.6 61.2 46.8 60.3 Table 1. Performances of entity representations on EntEval tasks. l etn l ctx Contexualized Entity Relationship Prediction (CP) CP = classify the correctness of statements for entity pairs. Gin and vermouth can make a martini. Entity Similarity and Relatedness (ESR) ESR = predict the similarity of two entities given descriptions. l lang (u 1:T )= - T X t=1 log p(u t+1 |u 1 ,...,u t ) + log p(u t-1 |u t ,...,u T ) Entity Relationship Typing (ERT) Score Entity Name - Apple Inc. 20 Steve Jobs … … 11 Microsoft … … 1 Ford Motor Company ERT = classify the types of relations between a pair of entities given descriptions. book.school_or_movement.associated_works English Renaissance Volpone Task Dataset #class NED Rare 4 CONLL-YAGO ≤ 30 Statistics of EntEval ET CAP EFP NED CP ERT ESR EntELMo Baseline 31.3 78.0 71.5 48.5 59.6 46.5 61.6 EntELMo 32.2 76.9 72.4 49.0 59.9 45.7 59.7 EntELMo w/o 33.2 73.5 71.1 48.9 59.4 44.6 53.3 EntELMo w/ 33.6 76.2 70.9 49.3 60.4 42.9 49.0 l ctx l etn Table 2. EntELMo w/ is trained with a modified version of where we only decode entity mentions instead of the whole context. Static vs non-static entity representations CONLL-YAGO ELMo 71.2 Gupta et al. 2017 65.1 Ganea and Hofmann, 2017 66.7 Scan to check out the code and data Task CAP CP EFP ET ESR ERT #class 2 2 2 10331 N/A 626 Dataset References § ET: Ultra-fine entity typing. § CAP: PreCo: A large-scale dataset in preschool vocabulary for coref resolution. § CP: Conceptnet 5.5: An open multilingual graph of general knowledge. § NED: Robust disambiguation of named entities in text. § NED: Rare entity prediction with hierarchical lstms using external descriptions. § ESR: Kore: keyphrase overlap relatedness for entity disambiguation. § ESR: Jointly embedding entities and text with distant supervision. § ERT: Freebase: a collaboratively created graph database for structuring human knowledge.

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Page 1: EntEval: A Holistic Evaluation Benchmark for Entity ...mchen/papers/mchen+etal.emnlp19.poster.pdfTable 2. EntELMow/ is trained with a modified version of where we only decode entity

C. China_men's_national_basketball_team: The Chinese men's national basketball team represents the …

Neural Networks

EntEval: A Holistic Evaluation Benchmark for Entity RepresentationsMingda Chen*1, Zewei Chu*2, Yang Chen4, Karl Stratos3, Kevin Gimpel1

1Toyota Technological Institute at Chicago 2University of Chicago 3Rutgers University 4Ohio State University

Learning Entity Representations

Entity Fixed-length vectorWe are interested in two approaches:o Contextualized entity representations (CER) that

encode an entity based on the context it appears regardless of whether the entity is seen before.

o Descriptive entity representations (DER) that rely on entries in Wikipedia.

EntEval§ 7 probing task groups.

Entity Typing (ET)ET = assign types to an entity given only the mention context.

Logic was established as a discipline by Aristotle, who established its fundamental place in philosophy.

Wisdom University Philosophy Accident …

Coreference Arc Prediction (CAP)

CAP = classify if two entities are the same given context

Revenues of $14.5 billion were posted by [Dell]. [The company] ...

EntEval cont.

Experiment Results

Entity Factuality Prediction (EFP)

EFP = classify the correctness of statements for entities.

TD Garden has held Bruins games.

Named Entity Disambiguation (NED)

NED = link a named-entity mention to its entry in a knowledge base.

SOCCER - JAPAN GET LUCKY WIN, CHINA IN SURPRISE DEFEAT.

D. China_PR_national_football_team: The Chinese national football team recognized as China PR by FIFA …

A. China: China is a country in East Asia …B. Porcelain: Porcelain is a ceramic material …

*Equal Contribution. Listed in alphabetical order.

Hyperlink-Based TrainingGiven a context sentence with mention span and a description sentence We use the same bidirectional language modeling loss

as in ELMo, where

x1:Tx<latexit sha1_base64="7yyKykH3Hv7WCjEmC7CZ4+/7QTs=">AAAB8HicbVBNSwMxEJ2tX7V+VT16CRbBU9mtguKp6MVjhX5JuyzZNG1Dk+ySZKVl6a/w4kERr/4cb/4b03YP2vpg4PHeDDPzwpgzbVz328mtrW9sbuW3Czu7e/sHxcOjpo4SRWiDRDxS7RBrypmkDcMMp+1YUSxCTlvh6G7mt56o0iySdTOJqS/wQLI+I9hY6XEcpN5NPRhPg2LJLbtzoFXiZaQEGWpB8avbi0giqDSEY607nhsbP8XKMMLptNBNNI0xGeEB7VgqsaDaT+cHT9GZVXqoHylb0qC5+nsixULriQhtp8BmqJe9mfif10lM/9pPmYwTQyVZLOonHJkIzb5HPaYoMXxiCSaK2VsRGWKFibEZFWwI3vLLq6RZKXsX5crDZal6m8WRhxM4hXPw4AqqcA81aAABAc/wCm+Ocl6cd+dj0Zpzsplj+APn8weLt5A9</latexit>

(i, j)<latexit sha1_base64="uWKFI3ctDetb1Qic4b2TEdjGxEo=">AAAB7HicbVBNSwMxEJ2tX7V+VT16CRahgpTdKuix6MVjBbcttEvJptk2NpssSVYoS3+DFw+KePUHefPfmLZ70OqDgcd7M8zMCxPOtHHdL6ewsrq2vlHcLG1t7+zulfcPWlqmilCfSC5VJ8Saciaob5jhtJMoiuOQ03Y4vpn57UeqNJPi3kwSGsR4KFjECDZW8qvs7OG0X664NXcO9Jd4OalAjma//NkbSJLGVBjCsdZdz01MkGFlGOF0WuqlmiaYjPGQdi0VOKY6yObHTtGJVQYoksqWMGiu/pzIcKz1JA5tZ4zNSC97M/E/r5ua6CrImEhSQwVZLIpSjoxEs8/RgClKDJ9Ygoli9lZERlhhYmw+JRuCt/zyX9Kq17zzWv3uotK4zuMowhEcQxU8uIQG3EITfCDA4Ale4NURzrPz5rwvWgtOPnMIv+B8fAPFPo4A</latexit>

y1:Ty<latexit sha1_base64="0tpY2KEcJ7PNifAR5MbMWY9uaBc=">AAAB8HicbVBNSwMxEJ2tX7V+VT16CRbBU9mtguKp6MVjhX5JuyzZNG1Dk+ySZIVl6a/w4kERr/4cb/4b03YP2vpg4PHeDDPzwpgzbVz32ymsrW9sbhW3Szu7e/sH5cOjto4SRWiLRDxS3RBrypmkLcMMp91YUSxCTjvh5G7md56o0iySTZPG1Bd4JNmQEWys9JgGmXfTDNJpUK64VXcOtEq8nFQgRyMof/UHEUkElYZwrHXPc2PjZ1gZRjidlvqJpjEmEzyiPUslFlT72fzgKTqzygANI2VLGjRXf09kWGiditB2CmzGetmbif95vcQMr/2MyTgxVJLFomHCkYnQ7Hs0YIoSw1NLMFHM3orIGCtMjM2oZEPwll9eJe1a1buo1h4uK/XbPI4inMApnIMHV1CHe2hACwgIeIZXeHOU8+K8Ox+L1oKTzxzDHzifP47IkD8=</latexit>

llang(x1:Tx) + llang(y1:Ty )<latexit sha1_base64="F5YIyN/nCow8lWp3Jop+JovcB74=">AAACGnicbVDLSsNAFJ3UV62vqEs3wSK0CCWpguKq6MZlhb6gLWEynbZDJ5MwcyMNId/hxl9x40IRd+LGv3H6WNjWAxcO59zLvfd4IWcKbPvHyKytb2xuZbdzO7t7+wfm4VFDBZEktE4CHsiWhxXlTNA6MOC0FUqKfY/Tpje6m/jNRyoVC0QN4pB2fTwQrM8IBi25psPdDtAxJByLQVoYu4lzU3PHafF80YhnRpwWXTNvl+wprFXizEkezVF1za9OLyCRTwUQjpVqO3YI3QRLYITTNNeJFA0xGeEBbWsqsE9VN5m+llpnWulZ/UDqEmBN1b8TCfaVin1Pd/oYhmrZm4j/ee0I+tfdhIkwAirIbFE/4hYE1iQnq8ckJcBjTTCRTN9qkSGWmIBOM6dDcJZfXiWNcsm5KJUfLvOV23kcWXSCTlEBOegKVdA9qqI6IugJvaA39G48G6/Gh/E5a80Y85ljtADj+xevJqE+</latexit>

In addition, we define two bag-of-words reconstruction losseslctx = �

X

t

log q(xt|fELMo([BOD]y1:Ty , 1, Ty))

ldesc = �X

t

log q(yt|fELMo([BOC]x1:Tx , i, j))<latexit sha1_base64="QKU3v/rMTCLCZUwofchPn/GB508=">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</latexit>

The final training loss for EntELMo isllang(x1:Tx) + llang(y1:Ty ) + lctx + ldesc

<latexit sha1_base64="1gT6iow1nGsXNnWXzGQsS2m9svk=">AAACNXicbZDLSsNAFIYn9V5vVZdugkWoCCVRQXElunHhQsFaoS1hMj1th04mYeZEGkJeyo3v4UoXLhRx6ys4vSCt9cDAP/93DjPn9yPBNTrOq5WbmZ2bX1hcyi+vrK6tFzY273QYKwYVFopQ3ftUg+ASKshRwH2kgAa+gKrfvejz6gMozUN5i0kEjYC2JW9xRtFYXuFKeHWEHqaCynZW6nmpe3rr9bK9/UmQDEEyBhj2st9LEzTLvELRKTuDsqeFOxJFMqprr/Bcb4YsDkAiE1TrmutE2EipQs4EZPl6rCGirEvbUDNS0gB0Ix1sndm7xmnarVCZI9EeuOMTKQ20TgLfdAYUO/ov65v/sVqMrZNGymUUI0g2fKgVCxtDux+h3eQKGIrECMoUN3+1WYcqytAEnTchuH9XnhZ3B2X3sHxwc1Q8Ox/FsUi2yQ4pEZcckzNySa5JhTDySF7IO/mwnqw369P6GrbmrNHMFpko6/sH5YatWA==</latexit>

Special symbols prepended to sentences to distinguish descriptions from contexts.

ET CAP EFP NED CP ERT ESRGloVe 10.3 71.9 67.0 41.2 52.6 40.8 50.9BERT Base 32.0 80.6 74.8 50.6 65.6 42.2 28.8BERT Large 32.3 79.1 76.7 54.3 66.9 48.8 32.6ELMo 35.6 79.1 75.8 51.6 61.2 46.8 60.3

Table 1. Performances of entity representations on EntEval tasks.

letn<latexit sha1_base64="lp1P0OaZ62xO2Z71RSocs2sAsFg=">AAAB83icbVBNS8NAEN3Ur1q/qh69LBbBU0mqoMeiF48V7Ac0oWy203bpZhN2J2IJ/RtePCji1T/jzX/jts1BWx8MPN6bYWZemEhh0HW/ncLa+sbmVnG7tLO7t39QPjxqmTjVHJo8lrHuhMyAFAqaKFBCJ9HAolBCOxzfzvz2I2gjYvWAkwSCiA2VGAjO0Eq+7PkIT5gBqmmvXHGr7hx0lXg5qZAcjV75y+/HPI1AIZfMmK7nJhhkTKPgEqYlPzWQMD5mQ+haqlgEJsjmN0/pmVX6dBBrWwrpXP09kbHImEkU2s6I4cgsezPxP6+b4uA6yIRKUgTFF4sGqaQY01kAtC80cJQTSxjXwt5K+YhpxtHGVLIheMsvr5JWrepdVGv3l5X6TR5HkZyQU3JOPHJF6uSONEiTcJKQZ/JK3pzUeXHenY9Fa8HJZ47JHzifP7kSkiE=</latexit>

lctx<latexit sha1_base64="Cbak4mpUO4+SVMLSTEpexe/NlBA=">AAAB83icbVBNS8NAEN34WetX1aOXxSJ4KkkV9Fj04rGC/YAmlM122i7dbMLuRFpC/4YXD4p49c9489+4bXPQ1gcDj/dmmJkXJlIYdN1vZ219Y3Nru7BT3N3bPzgsHR03TZxqDg0ey1i3Q2ZACgUNFCihnWhgUSihFY7uZn7rCbQRsXrESQJBxAZK9AVnaCVfdn2EMWYcx9NuqexW3DnoKvFyUiY56t3Sl9+LeRqBQi6ZMR3PTTDImEbBJUyLfmogYXzEBtCxVLEITJDNb57Sc6v0aD/WthTSufp7ImORMZMotJ0Rw6FZ9mbif14nxf5NkAmVpAiKLxb1U0kxprMAaE9o4CgnljCuhb2V8iHTjKONqWhD8JZfXiXNasW7rFQfrsq12zyOAjklZ+SCeOSa1Mg9qZMG4SQhz+SVvDmp8+K8Ox+L1jUnnzkhf+B8/gDFNpIp</latexit>

Contexualized Entity Relationship Prediction (CP)

CP = classify the correctness of statements for entity pairs.

Gin and vermouth can make a martini.

Entity Similarity and Relatedness (ESR)ESR = predict the similarity of two entities given descriptions.

llang(u1:T ) = �TX

t=1

log p(ut+1|u1, . . . , ut) + log p(ut�1|ut, . . . , uT )<latexit sha1_base64="YjtudAtCMSnGpQJDnDBbdSiYy2w=">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</latexit>

Entity Relationship Typing (ERT)

Score Entity Name- Apple Inc.

20 Steve Jobs… …11 Microsoft… …1 Ford Motor Company

ERT = classify the types of relations between a pair of entities given descriptions.

book.school_or_movement.associated_worksEnglish Renaissance Volpone

Task Dataset #class

NED Rare 4CONLL-YAGO ≤ 30

Statistics of EntEval

ET CAP EFP NED CP ERT ESREntELMo Baseline 31.3 78.0 71.5 48.5 59.6 46.5 61.6EntELMo 32.2 76.9 72.4 49.0 59.9 45.7 59.7EntELMo w/o 33.2 73.5 71.1 48.9 59.4 44.6 53.3EntELMo w/ 33.6 76.2 70.9 49.3 60.4 42.9 49.0

lctx<latexit sha1_base64="Cbak4mpUO4+SVMLSTEpexe/NlBA=">AAAB83icbVBNS8NAEN34WetX1aOXxSJ4KkkV9Fj04rGC/YAmlM122i7dbMLuRFpC/4YXD4p49c9489+4bXPQ1gcDj/dmmJkXJlIYdN1vZ219Y3Nru7BT3N3bPzgsHR03TZxqDg0ey1i3Q2ZACgUNFCihnWhgUSihFY7uZn7rCbQRsXrESQJBxAZK9AVnaCVfdn2EMWYcx9NuqexW3DnoKvFyUiY56t3Sl9+LeRqBQi6ZMR3PTTDImEbBJUyLfmogYXzEBtCxVLEITJDNb57Sc6v0aD/WthTSufp7ImORMZMotJ0Rw6FZ9mbif14nxf5NkAmVpAiKLxb1U0kxprMAaE9o4CgnljCuhb2V8iHTjKONqWhD8JZfXiXNasW7rFQfrsq12zyOAjklZ+SCeOSa1Mg9qZMG4SQhz+SVvDmp8+K8Ox+L1jUnnzkhf+B8/gDFNpIp</latexit>

letn<latexit sha1_base64="lp1P0OaZ62xO2Z71RSocs2sAsFg=">AAAB83icbVBNS8NAEN3Ur1q/qh69LBbBU0mqoMeiF48V7Ac0oWy203bpZhN2J2IJ/RtePCji1T/jzX/jts1BWx8MPN6bYWZemEhh0HW/ncLa+sbmVnG7tLO7t39QPjxqmTjVHJo8lrHuhMyAFAqaKFBCJ9HAolBCOxzfzvz2I2gjYvWAkwSCiA2VGAjO0Eq+7PkIT5gBqmmvXHGr7hx0lXg5qZAcjV75y+/HPI1AIZfMmK7nJhhkTKPgEqYlPzWQMD5mQ+haqlgEJsjmN0/pmVX6dBBrWwrpXP09kbHImEkU2s6I4cgsezPxP6+b4uA6yIRKUgTFF4sGqaQY01kAtC80cJQTSxjXwt5K+YhpxtHGVLIheMsvr5JWrepdVGv3l5X6TR5HkZyQU3JOPHJF6uSONEiTcJKQZ/JK3pzUeXHenY9Fa8HJZ47JHzifP7kSkiE=</latexit>

Table 2. EntELMo w/ is trained with a modified version of where we only decode entity mentions instead of the whole context.

Static vs non-static entity representations

CONLL-YAGOELMo 71.2Gupta et al. 2017 65.1Ganea and Hofmann, 2017 66.7

Scan to check out the code and data

Task CAP CP EFP ET ESR ERT

#class 2 2 2 10331 N/A 626

Dataset References§ ET: Ultra-fine entity typing.§ CAP: PreCo: A large-scale dataset in preschool vocabulary for coref resolution.§ CP: Conceptnet 5.5: An open multilingual graph of general knowledge.§ NED: Robust disambiguation of named entities in text.§ NED: Rare entity prediction with hierarchical lstms using external descriptions. § ESR: Kore: keyphrase overlap relatedness for entity disambiguation. § ESR: Jointly embedding entities and text with distant supervision.§ ERT: Freebase: a collaboratively created graph database for structuring human

knowledge.

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