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Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling
Luheng He*, Kenton Lee*, Omer Levy+, Luke Zettlemoyer University of Washington
1
*Now at Google +Now at Facebook AI Research
Semantic Role Labeling (SRL)
2
ARG0
AM-COM
eater
comitative
ARG1 meal
I ate pizza with friends
• Find out “who did what to whom” in text • Capture predicate-argument structures
Semantic Role Labeling (SRL)
2
ARG0
AM-COM
eater
comitative
ARG1 meal
I ate pizza with friends
• Find out “who did what to whom” in text • Capture predicate-argument structures
ARG0 eater ARG1 meal
I ate pizza with friends
3
Input1
SRL as BIO Tagging
visitMany tourists Disney to meet their favorite cartoon characters
B-VB-A0 I-A0 B-A1B-AM-PRP
I-AM-PRP
I-AM-PRP
I-AM-PRP
I-AM-PRP
I-AM-PRP
Output1 ARG0 V ARG1 AM-PRP
Collobert et al., 2011, Zhou and Xu, 2015,
He et. al, 2017, inter alia
Needs target predicate as input! (Prior works typically used gold predicates)
4
Input1
SRL as BIO Tagging
visitMany tourists Disney to meet their favorite cartoon characters
B-VB-A0 I-A0 B-A1B-AM-PRP
I-AM-PRP
I-AM-PRP
I-AM-PRP
I-AM-PRP
I-AM-PRP
Output1 ARG0 V ARG1 AM-PRP
Input2 visitMany tourists Disney to meet their favorite cartoon characters
OB-A0 I-A0 O O B-V B-A1 I-A1 I-A1 I-A1
Output2 ARG0 V ARG1
Needs to re-run the tagger for each predicate
!5
SRL as Predicting Word-Span Relations
ARG0 ARG1AM-PRP
visitMany tourists Disney to meet their favorite cartoon characters
ARG0 ARG1
!5
SRL as Predicting Word-Span Relations
Advantages: * Jointly predict predicates * Span-level features
(similar to Punyakanok08, FitzGerald15, inter alia)
Challenge: * Too many possible edges (n2 argument spans x n predicates)
ARG0 ARG1AM-PRP
visitMany tourists Disney to meet their favorite cartoon characters
ARG0 ARG1
Our Model
6
Our Model: Overview
Input sentence
Word & CharEmbeddings
HighwayBiLSTMs
Many tourists visit Disney to meet their favorite cartoon characters
No predicate input!
7
Our Model: Overview
Input sentence
Word & CharEmbeddings
HighwayBiLSTMs
Many tourists visit Disney to meet their favorite cartoon characters
Span Representation
Many tourists
tourists visit Disney
Disney to meet their
their favorite cartoon
cartoon characters
8
(1) Construct span representations for all n2 spans!
Input sentence
Word & CharEmbeddings
HighwayBiLSTMs
Many tourists visit Disney to meet their favorite cartoon characters
Span Representation
Node & Edge Scores
LabelingSoftmax
9
(2) Local classifier over labels (including NULL) for all possible (predicate, argument) pairs
(1) Construct span representations for all n2 spans!
…
Our Model: Overview
Input sentence
Word & CharEmbeddings
HighwayBiLSTMs
Many tourists visit Disney to meet their favorite cartoon characters
Span Representation
Node & Edge Scores
LabelingSoftmax
10
(2) Local classifier over labels (including NULL) for all possible (predicate, argument) pairs
(1) Construct span representations for all n2 spans!
…(3) Greedy beam pruning for spans
Our Model: Overview
Input sentence
Word & CharEmbeddings
HighwayBiLSTMs
Many tourists visit Disney to meet their favorite cartoon characters
Span Representation
11
their favorite cartoon
(1) Span Representations (2) Local Label Classifiers
(3) Span Pruning
(Same as Lee et al., 2017)
Input sentence
Word & CharEmbeddings
HighwayBiLSTMs
Many tourists visit Disney to meet their favorite cartoon characters
Span Representation
11
LSTM boundary points
their favorite cartoon
(1) Span Representations (2) Local Label Classifiers
(3) Span Pruning
[BiLstm(w1 : wn)Start,BiLstm(w1 : wn)End]
(Same as Lee et al., 2017)
left context right context
Input sentence
Word & CharEmbeddings
HighwayBiLSTMs
Many tourists visit Disney to meet their favorite cartoon characters
Span Representation
12
LSTM boundary points
Attention over words
their favorite cartoon
(1) Span Representations (2) Local Label Classifiers
(3) Span Pruning
[BiLstm(w1 : wn)Start,BiLstm(w1 : wn)End]
XEnd
i=StartSoftMax(aStart : aEnd)i wi
(Same as Lee et al., 2017)
Disney to meet favorite cartoon charactersInput sentence
Word & CharEmbeddings
HighwayBiLSTMs
Many tourists visit
Span Representation
Node & Edge Scores
LabelingSoftmax
13
(1) Span Representations (2) Local Label Classifiers (3) Span
Pruning
predicateargument
ARG0ARG1ARG2
…AM-TMP
…𝜖 (No Edge)
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their
Disney to meet favorite cartoon charactersInput sentence
Word & CharEmbeddings
HighwayBiLSTMs
Many tourists visit
Span Representation
Node & Edge Scores
LabelingSoftmax
14
(1) Span Representations (2) Local Label Classifiers (3) Span
Pruning
their
meetmany tourists
ARG0ARG1ARG2
…AM-TMP
…𝜖 (No Edge)
Disney to meet favorite cartoon charactersInput sentence
Word & CharEmbeddings
HighwayBiLSTMs
Many tourists visit
Span Representation
Node & Edge Scores
LabelingSoftmax
15
(1) Span Representations (2) Local Label Classifiers (3) Span
Pruning
their
meetDisney
ARG0ARG1ARG2
…AM-TMP
…𝜖 (No Edge)
Disney to meet favorite cartoon charactersInput sentence
Word & CharEmbeddings
HighwayBiLSTMs
Many tourists visit
Span Representation
Node & Edge Scores
LabelingSoftmax
16
(1) Span Representations (2) Local Label Classifiers (3) Span
Pruning
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their
17
Many tourists meetSpan Representation
Pred./Arg. score
�a(“Many tourists”)<latexit sha1_base64="Ss5Tl4FoKSKJRKvsg4oMl04Ft/0=">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</latexit><latexit sha1_base64="Ss5Tl4FoKSKJRKvsg4oMl04Ft/0=">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</latexit><latexit sha1_base64="Ss5Tl4FoKSKJRKvsg4oMl04Ft/0=">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</latexit><latexit sha1_base64="Ss5Tl4FoKSKJRKvsg4oMl04Ft/0=">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</latexit>
�p(“meet”)
(1) Span Representations (2) Local Label Classifiers (3) Span
Pruning
�(pred, arg, l) = �a(arg) + �p(pred) + �(l)rel(arg, pred)
<latexit sha1_base64="F2SSBYlFAcaRsK5CIxHyBXBOdoE=">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</latexit><latexit sha1_base64="F2SSBYlFAcaRsK5CIxHyBXBOdoE=">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</latexit><latexit sha1_base64="F2SSBYlFAcaRsK5CIxHyBXBOdoE=">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</latexit>
�(pred, arg, l)<latexit sha1_base64="FDP4vzdLhw9DhfaiCwy1qR/1xlg=">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</latexit><latexit sha1_base64="FDP4vzdLhw9DhfaiCwy1qR/1xlg=">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</latexit><latexit sha1_base64="FDP4vzdLhw9DhfaiCwy1qR/1xlg=">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</latexit><latexit sha1_base64="FDP4vzdLhw9DhfaiCwy1qR/1xlg=">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</latexit>
17
Many tourists meetSpan Representation
Edge score
Pred./Arg. score
�(ARG1)rel (“Many tourists”, “meet”)�(ARG0)
rel (“Many tourists”, “meet”)
�a(“Many tourists”)<latexit sha1_base64="Ss5Tl4FoKSKJRKvsg4oMl04Ft/0=">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</latexit><latexit sha1_base64="Ss5Tl4FoKSKJRKvsg4oMl04Ft/0=">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</latexit><latexit sha1_base64="Ss5Tl4FoKSKJRKvsg4oMl04Ft/0=">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</latexit><latexit sha1_base64="Ss5Tl4FoKSKJRKvsg4oMl04Ft/0=">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</latexit>
�p(“meet”)
(1) Span Representations (2) Local Label Classifiers (3) Span
Pruning
�(pred, arg, l) = �a(arg) + �p(pred) + �(l)rel(arg, pred)
<latexit sha1_base64="F2SSBYlFAcaRsK5CIxHyBXBOdoE=">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</latexit><latexit sha1_base64="F2SSBYlFAcaRsK5CIxHyBXBOdoE=">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</latexit><latexit sha1_base64="F2SSBYlFAcaRsK5CIxHyBXBOdoE=">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</latexit>
�(pred, arg, l)<latexit sha1_base64="FDP4vzdLhw9DhfaiCwy1qR/1xlg=">AAACT3icbVFNSwMxEM3W7/pV9eglWAQFKbsi6LEoiBdRwarQLSWbnW6D2eySzIpl2X/kn9Gb6K/w4k1M2xX8Ggi8eTOZN3kJUikMuu6zU5mYnJqemZ2rzi8sLi3XVlavTJJpDi2eyETfBMyAFApaKFDCTaqBxYGE6+D2aFi/vgNtRKIucZBCJ2aREj3BGVqqWzv2077Yyv3RpPxUhEpEfTyUGRQ+wj3mdlpY7NBxwnRU2OSr/UwzFUEhi+1ure423FHQv8ArQZ2Ucd6tvflhwrMYFHLJjGl7boodK4CCSyiqfmYgZfyWRdC2ULEYTCcfyRZ00zIh7SXaHoV0xH6/kbPYmEEc2M6YYd/8rg3J/2rt8E6kptS6H4v93AR7B51cqDRDUHy8SC+TFBM6NJeGQgNHObCAcS3sWyjvM8042i+oWpO835b8BVe7Dc9teBd79WaztGuWrJMNskU8sk+a5ISckxbh5IE8kRfy6jw6785HpWytOCVYIz+iMvcJb4C3Hg==</latexit><latexit sha1_base64="FDP4vzdLhw9DhfaiCwy1qR/1xlg=">AAACT3icbVFNSwMxEM3W7/pV9eglWAQFKbsi6LEoiBdRwarQLSWbnW6D2eySzIpl2X/kn9Gb6K/w4k1M2xX8Ggi8eTOZN3kJUikMuu6zU5mYnJqemZ2rzi8sLi3XVlavTJJpDi2eyETfBMyAFApaKFDCTaqBxYGE6+D2aFi/vgNtRKIucZBCJ2aREj3BGVqqWzv2077Yyv3RpPxUhEpEfTyUGRQ+wj3mdlpY7NBxwnRU2OSr/UwzFUEhi+1ure423FHQv8ArQZ2Ucd6tvflhwrMYFHLJjGl7boodK4CCSyiqfmYgZfyWRdC2ULEYTCcfyRZ00zIh7SXaHoV0xH6/kbPYmEEc2M6YYd/8rg3J/2rt8E6kptS6H4v93AR7B51cqDRDUHy8SC+TFBM6NJeGQgNHObCAcS3sWyjvM8042i+oWpO835b8BVe7Dc9teBd79WaztGuWrJMNskU8sk+a5ISckxbh5IE8kRfy6jw6785HpWytOCVYIz+iMvcJb4C3Hg==</latexit><latexit sha1_base64="FDP4vzdLhw9DhfaiCwy1qR/1xlg=">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</latexit><latexit sha1_base64="FDP4vzdLhw9DhfaiCwy1qR/1xlg=">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</latexit>
18
Many tourists meetSpan Representation
Combined score
Softmax
Edge score
Pred./Arg. score
�(ARG1)rel (“Many tourists”, “meet”)�(ARG0)
rel (“Many tourists”, “meet”)
�a(“Many tourists”)<latexit sha1_base64="Ss5Tl4FoKSKJRKvsg4oMl04Ft/0=">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</latexit><latexit sha1_base64="Ss5Tl4FoKSKJRKvsg4oMl04Ft/0=">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</latexit><latexit sha1_base64="Ss5Tl4FoKSKJRKvsg4oMl04Ft/0=">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</latexit><latexit sha1_base64="Ss5Tl4FoKSKJRKvsg4oMl04Ft/0=">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</latexit>
�p(“meet”)
�(“Many tourists”, “meet”,ARG0) �(“Many tourists”, “meet”,ARG1)
�(“Many tourists”, “meet”, ✏) = 0
ARG0
(1) Span Representations (2) Local Label Classifiers (3) Span
Pruning
�(pred, arg, l) = �a(arg) + �p(pred) + �(l)rel(arg, pred)
<latexit sha1_base64="F2SSBYlFAcaRsK5CIxHyBXBOdoE=">AAADGXichVLLattAFB2pr9R9Oc2ym6Gm4NBg5FBosiiYdpNNqQt1E7BcMxpdS0NGIzFzlcaI+ZN2m/xHV6Xbrvob/YKOZYXGTiAXBs59nDNnLhMVUhgMgj+ef+v2nbv3Nu63Hjx89PhJe/PpZ5OXmsOI5zLXRxEzIIWCEQqUcFRoYFkk4TA6frfoH56ANiJXn3BewCRjiRIzwRm60nTTa4dFKrpVWEtV70WsRJLiW1mCDRFOsXJysd2hy4TpxLrkYvyDZioBK+02fUNb4TAV06oZtPZ60ctC2/TlKqu4gVV7+U/7UnXXnFBnxV6oaZDW0htt7KyKT9udoBfUQa+CfgM6pInhtP03jHNeZqCQS2bMuB8UOHHSKLgE2wpLAwXjxyyBsYOKZWAmVe3J0heuEtNZrt1RSOvqZUbFMmPmWeQmM4apWe8titf1xvGJKExz1+nyslUnONubVEIVJYLiSyOzUlLM6eKf0Fho4CjnDjCuhXsL5SnTjKP7TS23pP76Sq6C0W5vvxd8fNUZDJptbZBn5Dnpkj55TQbkgAzJiHDvq/fdO/PO/W/+D/+n/2s56nsNZ4ushP/7H61XBJ4=</latexit><latexit sha1_base64="F2SSBYlFAcaRsK5CIxHyBXBOdoE=">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</latexit><latexit sha1_base64="F2SSBYlFAcaRsK5CIxHyBXBOdoE=">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</latexit>
Input sentence
Word & CharEmbeddings
HighwayBiLSTMs
Many tourists visit Disney to meet their favorite cartoon characters
Span Representation
Many tourists
tourists visit Disney
Disney to meet their
their favorite cartoon
cartoon characters
19
(1) Span Representations
(2) Local Label Classifiers (3) Span Pruning
O(n2) arguments, O(n) predicates, —> O(n3) edges!
Input sentence
Word & CharEmbeddings
HighwayBiLSTMs
Many tourists visit Disney to meet their favorite cartoon characters
Span Representation
Many tourists
tourists visit Disney
Disney to meet their
their favorite cartoon
cartoon characters
Only keep top O(n) spans using their unary scores
20
(1) Span Representations
(2) Local Label Classifiers (3) Span Pruning
�a(“many tourists”) = 2.5<latexit sha1_base64="6qwwvAYOfJnvm20og4XIEFylnCM=">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</latexit><latexit sha1_base64="6qwwvAYOfJnvm20og4XIEFylnCM=">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</latexit><latexit sha1_base64="6qwwvAYOfJnvm20og4XIEFylnCM=">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</latexit><latexit sha1_base64="6qwwvAYOfJnvm20og4XIEFylnCM=">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</latexit>
�a(“tourists visit Disney”) = �0.8<latexit sha1_base64="Hd3Kgz6SWV4jo9WWpZUelG17YmI=">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</latexit><latexit sha1_base64="Hd3Kgz6SWV4jo9WWpZUelG17YmI=">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</latexit><latexit sha1_base64="Hd3Kgz6SWV4jo9WWpZUelG17YmI=">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</latexit><latexit sha1_base64="Hd3Kgz6SWV4jo9WWpZUelG17YmI=">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</latexit>
...<latexit sha1_base64="7Iu17wX+vYl0vGV+JihJ1bF/x/o=">AAACEXicbVDLSsNAFJ3UV62vqks3wSK4CokIuiy4cVnBPrANZTK5bYdOJmHmprSEfoVu9T/ciVu/wN/wC5y2WdjWAwOHc+7lnjlBIrhG1/22ChubW9s7xd3S3v7B4VH5+KSh41QxqLNYxKoVUA2CS6gjRwGtRAGNAgHNYHg385sjUJrH8hEnCfgR7Uve44yikZ46CGPMHMeZdssV13HnsNeJl5MKyVHrln86YczSCCQyQbVue26CfkYVciZgWuqkGhLKhrQPbUMljUD72Tzx1L4wSmj3YmWeRHuu/t3IaKT1JArMZERxoFe9mfif1w5HPNH5rfHi2HIS7N36GZdJiiDZIkgvFTbG9qweO+QKGIqJIZQpbv5iswFVlKEpsWRK8lYrWSeNK8dzHe/hulKt5nUVyRk5J5fEIzekSu5JjdQJI5K8kFfyZj1b79aH9bkYLVj5zilZgvX1C658nj8=</latexit><latexit sha1_base64="7Iu17wX+vYl0vGV+JihJ1bF/x/o=">AAACEXicbVDLSsNAFJ3UV62vqks3wSK4CokIuiy4cVnBPrANZTK5bYdOJmHmprSEfoVu9T/ciVu/wN/wC5y2WdjWAwOHc+7lnjlBIrhG1/22ChubW9s7xd3S3v7B4VH5+KSh41QxqLNYxKoVUA2CS6gjRwGtRAGNAgHNYHg385sjUJrH8hEnCfgR7Uve44yikZ46CGPMHMeZdssV13HnsNeJl5MKyVHrln86YczSCCQyQbVue26CfkYVciZgWuqkGhLKhrQPbUMljUD72Tzx1L4wSmj3YmWeRHuu/t3IaKT1JArMZERxoFe9mfif1w5HPNH5rfHi2HIS7N36GZdJiiDZIkgvFTbG9qweO+QKGIqJIZQpbv5iswFVlKEpsWRK8lYrWSeNK8dzHe/hulKt5nUVyRk5J5fEIzekSu5JjdQJI5K8kFfyZj1b79aH9bkYLVj5zilZgvX1C658nj8=</latexit><latexit sha1_base64="7Iu17wX+vYl0vGV+JihJ1bF/x/o=">AAACEXicbVDLSsNAFJ3UV62vqks3wSK4CokIuiy4cVnBPrANZTK5bYdOJmHmprSEfoVu9T/ciVu/wN/wC5y2WdjWAwOHc+7lnjlBIrhG1/22ChubW9s7xd3S3v7B4VH5+KSh41QxqLNYxKoVUA2CS6gjRwGtRAGNAgHNYHg385sjUJrH8hEnCfgR7Uve44yikZ46CGPMHMeZdssV13HnsNeJl5MKyVHrln86YczSCCQyQbVue26CfkYVciZgWuqkGhLKhrQPbUMljUD72Tzx1L4wSmj3YmWeRHuu/t3IaKT1JArMZERxoFe9mfif1w5HPNH5rfHi2HIS7N36GZdJiiDZIkgvFTbG9qweO+QKGIqJIZQpbv5iswFVlKEpsWRK8lYrWSeNK8dzHe/hulKt5nUVyRk5J5fEIzekSu5JjdQJI5K8kFfyZj1b79aH9bkYLVj5zilZgvX1C658nj8=</latexit><latexit sha1_base64="7Iu17wX+vYl0vGV+JihJ1bF/x/o=">AAACEXicbVDLSsNAFJ3UV62vqks3wSK4CokIuiy4cVnBPrANZTK5bYdOJmHmprSEfoVu9T/ciVu/wN/wC5y2WdjWAwOHc+7lnjlBIrhG1/22ChubW9s7xd3S3v7B4VH5+KSh41QxqLNYxKoVUA2CS6gjRwGtRAGNAgHNYHg385sjUJrH8hEnCfgR7Uve44yikZ46CGPMHMeZdssV13HnsNeJl5MKyVHrln86YczSCCQyQbVue26CfkYVciZgWuqkGhLKhrQPbUMljUD72Tzx1L4wSmj3YmWeRHuu/t3IaKT1JArMZERxoFe9mfif1w5HPNH5rfHi2HIS7N36GZdJiiDZIkgvFTbG9qweO+QKGIqJIZQpbv5iswFVlKEpsWRK8lYrWSeNK8dzHe/hulKt5nUVyRk5J5fEIzekSu5JjdQJI5K8kFfyZj1b79aH9bkYLVj5zilZgvX1C658nj8=</latexit>
Results & Analysis
21
F1
60
65
70
75
80
85
90
CoNL05 WSJ Test CoNL05 Brown Test CoNLL2012 (OntoNotes)
79.8
70.8
82.5
76.8
68.5
81.2
He17 Ours He17 (Ensemble) Ours+ELMo
22
End-to-End SRL Results
BIO-based, pipelined predicate ID
F1
60
65
70
75
80
85
90
CoNL05 WSJ Test CoNL05 Brown Test CoNLL2012 (OntoNotes)
82.9
76.1
86
78.4
70.1
82.779.8
70.8
82.5
76.8
68.5
81.2
He17 Ours He17 (Ensemble) Ours+ELMo
23
End-to-End SRL Results
With ELMo, over 3 points improvement over SotA ensemble!
*ELMo: Deep Contextualized Word Representations, Peters et al., 2018
24
Span-based vs. BIO
BIO (He17)
Span-based (this work)
Inputs (Sentence, Predicate) Sentence
Predicate Identification Pipelined Joint
Global Consistency
Long-range Dependency
24
Span-based vs. BIO
BIO (He17)
Span-based (this work)
Inputs (Sentence, Predicate) Sentence
Predicate Identification Pipelined Joint
Global Consistency
Long-range Dependency
Due to the strong independence assumption we make.
24
Span-based vs. BIO
BIO (He17)
Span-based (this work)
Inputs (Sentence, Predicate) Sentence
Predicate Identification Pipelined Joint
Global Consistency
Long-range Dependency
Due to the strong independence assumption we make.
By allowing direct interaction between predicates and arguments
Conclusion
25
• Joint prediction of predicates and arguments
Conclusion
25
• Our recipe: 1. Contextualized span
representations 2. Local label classifiers 3. Greedy span pruning
• Joint prediction of predicates and arguments
Conclusion
25
• Our recipe: 1. Contextualized span
representations 2. Local label classifiers 3. Greedy span pruning
• Joint prediction of predicates and arguments
• Future work: Improve global consistency, use span representations for downstream tasks, etc.
THANK YOU!
26
Code and pertained models: https://github.com/luheng/lsgn
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