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=">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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>

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=">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</latexit><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=">AAADGXichVLLattAFB2pr9R9Oc2ym6Gm4NBg5FBosiiYdpNNqQt1E7BcMxpdS0NGIzFzlcaI+ZN2m/xHV6Xbrvob/YKOZYXGTiAXBs59nDNnLhMVUhgMgj+ef+v2nbv3Nu63Hjx89PhJe/PpZ5OXmsOI5zLXRxEzIIWCEQqUcFRoYFkk4TA6frfoH56ANiJXn3BewCRjiRIzwRm60nTTa4dFKrpVWEtV70WsRJLiW1mCDRFOsXJysd2hy4TpxLrkYvyDZioBK+02fUNb4TAV06oZtPZ60ctC2/TlKqu4gVV7+U/7UnXXnFBnxV6oaZDW0htt7KyKT9udoBfUQa+CfgM6pInhtP03jHNeZqCQS2bMuB8UOHHSKLgE2wpLAwXjxyyBsYOKZWAmVe3J0heuEtNZrt1RSOvqZUbFMmPmWeQmM4apWe8titf1xvGJKExz1+nyslUnONubVEIVJYLiSyOzUlLM6eKf0Fho4CjnDjCuhXsL5SnTjKP7TS23pP76Sq6C0W5vvxd8fNUZDJptbZBn5Dnpkj55TQbkgAzJiHDvq/fdO/PO/W/+D/+n/2s56nsNZ4ushP/7H61XBJ4=</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