Ordinal Common-sense Inference
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1 Ordinal Common-sense Inference Sheng Zhang Rachel Rudinger Kevin Duh Benjamin Van Durme Johns Hopkins University Transactions of the Association for Computational Linguistics Vancouver, July 31st, 2017
2 New task: Ordinal Common-sense Inference New corpus: JOCI [joe-cee] 2
3 New task: Ordinal Common-sense Inference New corpus: JOCI [joe-cee] 39k examples (Context, Hypothesis, Subjective likelihood) 3
4 We use words to talk about the world. Therefore, to understand what words mean, we must have a prior explication of how we view the world. -- Hobbs (1987) 4
5 Common Sense for language Definitions Common-sense inference is Prevalent Characterize common-sense inference New task: Ordinal Common-sense Inference Common Sense from language 5
6 Common Sense for language Definitions Common-sense inference is Prevalent Characterize common-sense inference New task: Ordinal Common-sense Inference Common Sense from language 6
7 Definitions 7
8 Shared Knowledge If I know p You know p 8
9 Shared Knowledge If I know p You know p I know that you know p You know that I know p 9
10 Shared Knowledge If I know p You know p I know that you know p You know that I know p Then p is shared knowledge 10
11 Background Knowledge If p is shared across some group, then we say that p is background knowledge. 11
12 Common Sense When that group is really big then p is called: commonly known background knowledge 12
13 Common Sense When that group is really big then p is called: commonly known background knowledge or just simply: common sense 13
14 Common Sense is Prevalent 14
15 Example Rachel walked up to a house. She knocked on the door. 15
16 Example Rachel walked up to a house. She knocked on the door. What door? 16
17 Example Rachel walked up to a house. She knocked on the door. What door? Houses have doors. 17
18 Common-sense Inference 18
19 Inferences though conveyed by language draw on one s knowledge of natural objects and events that goes beyond one s knowledge of language itself. -- Clark (1975) 19
20 a program has common sense if it automatically deduces for itself a sufficiently wide class of immediate consequences of anything it is told and what it already knows -- McCarthy (1959) 20
21 Characterize Common-sense Inference 21
22 Textual Inference Recognizing Textual Entailment (RTE) 22
23 Textual Inference Recognizing Textual Entailment (RTE) (Dagan et al. 2006) 23
24 Textual Inference Recognizing Textual Entailment (RTE) Text: China launched a meteorological satellite into orbit Wednesday. (Example adapted from Clark et al., 2003) 24
25 Textual Inference Recognizing Textual Entailment (RTE) Text: China launched a meteorological satellite into orbit Wednesday. Hypothesis: China launched a satellite. China owns the satellite. The orbit is around Neptune. China canceled the satellite launch. (Example adapted from Clark et al., 2003) 25
26 Textual Inference Recognizing Textual Entailment (RTE) Text: China launched a meteorological satellite into orbit Wednesday. Hypothesis: China launched a satellite. China owns the satellite. Entailment The orbit is around Neptune. China canceled the satellite launch. Contradiction (Example adapted from Clark et al., 2003) 26
27 Textual Inference Recognizing Textual Entailment (RTE) Text: China launched a meteorological satellite into orbit Wednesday. Hypothesis: China launched a satellite. China owns the satellite. Entailment? The orbit is around Neptune.? China canceled the satellite launch. Contradiction (Example adapted from Clark et al., 2003) 27
28 Textual Inference Recognizing Textual Entailment (RTE) Text: China launched a meteorological satellite into orbit Wednesday. Hypothesis: China launched a satellite. China owns the satellite. Entailment Neutral The orbit is around Neptune. Neutral China canceled the satellite launch. Contradiction (Example adapted from Clark et al., 2003) 28
29 China launched a meteorological satellite into orbit Wednesday. China owns the satellite. The orbit is around Neptune. Neutral Neutral 29
30 Non-entailing Inference China launched a meteorological satellite into orbit Wednesday. China owns the satellite. The orbit is around Neptune. Non-entailing Inference 30
31 Non-entailing Inference Not logically entailed, but more or less likely to be true in a given context. China launched a meteorological satellite into orbit Wednesday. China owns the satellite. The orbit is around Neptune. Non-entailing Inference 31
32 Non-entailing Inference Context Entailment / Contradiction Hypothesis China launched a meteorological satellite into orbit Wednesday. China owns the satellite. The orbit is around Neptune. Non-entailing Inference 32
33 Non-entailing Inference Context Entailment / Contradiction Hypothesis China launched a meteorological satellite into orbit Wednesday. China owns the satellite. The orbit is around Neptune. Non-entailing Inference 33
34 Non-entailing Inference Context Entailment / Contradiction Hypothesis China launched a meteorological satellite into orbit Wednesday. China owns the satellite. The orbit is around Neptune. Non-entailing Inference 34
35 Non-entailing Inference Context Entailment / Contradiction Hypothesis China launched a meteorological satellite into orbit Wednesday. China owns the satellite. The orbit is around Neptune. Non-entailing Inference 35
36 Non-entailing Inference Context Subjective Likelihood Hypothesis China launched a meteorological satellite into orbit Wednesday. China owns the satellite. The orbit is around Neptune. Non-entailing Inference 36
37 Non-entailing Inference Context Subjective Likelihood Hypothesis 37
38 Non-entailing Inference Context Subjective Likelihood Hypothesis Continuous Category (Saurí and Pustejovsky, 2009) 38
39 Non-entailing Inference Context Subjective Likelihood Hypothesis Very likely Likely Plausible Technically possible Impossible Discreet values (Saurí and Pustejovsky, 2009) 39
40 Ordinal Common-sense Inference 40
41 Ordinal Common-sense Inference Context: China launched a meteorological satellite into orbit Wednesday. (Example adapted from Clark et al., 2003) 41
42 Ordinal Common-sense Inference Context: China launched a meteorological satellite into orbit Wednesday. Hypothesis: There was a rocket launch. (Example adapted from Clark et al., 2003) 42
43 Ordinal Common-sense Inference Context: China launched a meteorological satellite into orbit Wednesday. Hypothesis: There was a rocket launch. Very likely (Example adapted from Clark et al., 2003) 43
44 Ordinal Common-sense Inference Context: China launched a meteorological satellite into orbit Wednesday. Hypothesis: There was a rocket launch. China owns the satellite. Very likely Likely (Example adapted from Clark et al., 2003) 44
45 Ordinal Common-sense Inference Context: China launched a meteorological satellite into orbit Wednesday. Hypothesis: There was a rocket launch. China owns the satellite. The satellite weighs 10,000 pounds. Very likely Likely Plausbile (Example adapted from Clark et al., 2003) 45
46 Ordinal Common-sense Inference Context: China launched a meteorological satellite into orbit Wednesday. Hypothesis: There was a rocket launch. China owns the satellite. The satellite weighs 10,000 pounds. The orbit is around Neptune. Very likely Likely Plausbile Tech-possible (Example adapted from Clark et al., 2003) 46
47 Ordinal Common-sense Inference Context: China launched a meteorological satellite into orbit Wednesday. Hypothesis: There was a rocket launch. China owns the satellite. The satellite weighs 10,000 pounds. The orbit is around Neptune. The satellite was caught by a bird. Very likely Likely Plausbile Tech-possible Impossible (Example adapted from Clark et al., 2003) 47
48 Common Sense for language Definitions Common-sense inference is Prevalent Characterize common-sense inference New task: Ordinal Common-sense Inference Common Sense from language 48
49 Common Sense for language Definitions Common-sense inference is Prevalent Characterize common-sense inference New task: Ordinal Common-sense Inference Common Sense from language 49
50 Human Elicitation Text Mining Approaches 50
51 Human Elicitation 51
52 Human Elicitation Expert elicitation is expensive. FRACAS (Cooper et al., 1996) 52
53 Human Elicitation Expert elicitation is expensive. FRACAS (Cooper et al., 1996) Crowdsourced elicitation is scalable. SNLI (Bowman et al., 2015) ROCStories (Mostafazadeh et al., 2016) 53
54 Elicitation Bias Features such as <is larger than a tulip> or <moves faster than an infant>, although logically possible, do not occur in human responses people are capable of verifying that a <dog is larger than a pencil>. -- McRae et al. (2005) 54
55 Text Mining 55
56 Text Mining Reporting Bias: P(people write about X) P(X in the real world) (Van Durme 2010, Gordon and Van Durme, 2013) 56
57 Text Mining Reporting Bias: P(people write about X) P(X in the real world) Frequencies of A person may x (Van Durme 2010, Gordon and Van Durme, 2013) 57
58 Text Mining Reporting Bias: P(people write about X) P(X in the real world) Frequencies of A person may x (Van Durme 2010, Gordon and Van Durme, 2013) 58
59 No elicitation bias No reporting bias Our Approach (Data for Ordinal Common-sense Inference) (Schubert 2002, Van Durme and Schubert 2008) 59
60 Text Automated Construction KB Common-sense Inference Candidates Context Hypothesis Crowdsourced Annotation Ordinal Common-sense Inference 60
61 Text Automated Construction KB Common-sense Inference Candidates Context Hypothesis Crowdsourced Annotation Ordinal Common-sense Inference 61
62 Text Automated Construction KB Common-sense Inference Candidates Context Hypothesis Crowdsourced Annotation Ordinal Common-sense Inference 62
63 Automated Construction Text KB 63
64 Text Automated Construction Abstracted Propositions [person] borrow [book] from [library] KB 64
65 Automated Construction Text Abstracted Propositions [person] borrow [book] from [library] KB book person borrow from library person buy Propositional Templates 65
66 Automated Construction Text Abstracted Propositions [person] borrow [book] from [library] No frequency KB book person borrow from library person buy Propositional Templates 66
67 Automated Construction Text Abstracted Propositions [person] borrow [book] from [library] KB book person borrow from library person buy Propositional Templates publication.n.01 collection.n.02 magazine.n.01 book.n.01 67
68 Text Automated Construction Abstracted Propositions [person] borrow [book] from [library] KB book person borrow from library person buy Propositional Templates publication.n.01 collection.n.02 hyponym magazine.n.01 hyponym book.n.01 hyponym 68
69 Automated Construction Text book [person] borrow [book] from [library] person borrow from library person buy Propositional Templates Abstracted Propositions magazine.n.01 publication.n.01 person buy yes person subscribe to yes hyponym Decision Trees no book.n.01 no KB collection.n.02 person borrow from library yes hyponym hyponym 69
70 Automated Construction Text book [person] borrow [book] from [library] person borrow from library person buy Propositional Templates Abstracted Propositions magazine.n.01 publication.n.01 person buy yes person subscribe to yes hyponym Decision Trees no book.n.01 no KB collection.n.02 person borrow from library yes hyponym hyponym 70
71 Automated Construction Text book [person] borrow [book] from [library] person borrow from library person buy Propositional Templates Abstracted Propositions magazine.n.01 publication.n.01 person buy yes person subscribe to yes hyponym Decision Trees no book.n.01 no KB collection.n.02 person borrow from library yes hyponym hyponym 71
72 Common-sense Inference Candidates KB 72
73 Common-sense Inference Candidates Context: A child is reading books on a park bench. KB 73
74 Common-sense Inference Candidates Context: A child is reading books on a park bench. KB 74
75 Common-sense Inference Candidates Context: A child is reading books on a park bench. KB be borrowed from a library 75
76 Common-sense Inference Candidates Context: A child is reading books on a park bench. KB be borrowed from a library Hypothesis: The books are borrowed from a library. 76
77 Common-sense Inference Candidates Context: A child is reading books on a park bench. Hypothesis: The books are borrowed from a library. 77
78 Automatic Generation Common-sense Inference Candidates 78
79 Automatic Generation Common-sense Inference Candidates Text KB 79
80 Automatic Generation Common-sense Inference Candidates Text KB Common-sense Inference Candidates Context Hypothesis 80
81 Automatic Generation Common-sense Inference Candidates Text KB SNLI Common-sense Inference Candidates Context Hypothesis 81
82 Common-sense Inference Candidates Context Hypothesis 82
83 Ordinal Label Annotation Common-sense Inference Candidates Context Hypothesis Crowdsourced Annotation Ordinal Common-sense Inference 83
84 Amazon Mechanical Turk Initial Sentence: Mary saw a car. 1. The following statements is tech possible to be true during or shortly after the context of the initial sentence. The car was made of gold. This statement does not make sense. 84
85 Amazon Mechanical Turk Initial Sentence: Mary saw a car. 1. The following statements is tech possible to be true during or shortly after the context of the initial sentence. The car was made of gold. This statement does not make sense. Context 85
86 Amazon Mechanical Turk Initial Sentence: Mary saw a car. 1. The following statements is tech possible to be true during or shortly after the context of the initial sentence. The car was made of gold. This statement does not make sense. Hypothesis 86
87 Amazon Mechanical Turk Initial Sentence: Mary saw a car. 1. The following statements is tech possible to be true during or shortly after the context of the initial sentence. The car was made of gold. This statement does not make sense. 87
88 Amazon Mechanical Turk Initial Sentence: Mary saw a car. 1. The following statements is tech possible to be true during or shortly after the context of the initial sentence. The car was made of gold. This statement does not make sense. 88
89 Common-sense Inference Candidates Context Hypothesis Crowdsourced Annotation Ordinal Common-sense Inference JOCI corpus (JHU Ordinal Common-sense Inference) 89
90 JOCI 39k (Context, Hypothesis, Label) 90
91 JOCI 39k (Context, Hypothesis, Label) Context Hypothesis Label Major SNLI/ROCStories Our Approach 91
92 JOCI 39k (Context, Hypothesis, Label) Context Hypothesis Label Major SNLI/ROCStories Our Approach Comparing SNLI ROCStories COPA SNLI ROCStories COPA 92
93 JOCI 93
94 JOCI Scalable & Reliable 94
95 Scalable & Reliable Average annotation time per example 20.71s Average cost per example 1.99 Average Cohen s κ
96 Scalable & Reliable Average annotation time per example 20.71s Average cost per example 1.99 Average Cohen s κ
97 Scalable & Reliable Average annotation time per example 20.71s Average cost per example 1.99 Average Cohen s κ
98 Scalable & Reliable Average annotation time per example 20.71s Average cost per example 1.99 Average Cohen s κ
99 JOCI Scalable & Reliable 99
100 JOCI Scalable & Reliable Capable of evaluating/training inference systems 100
101 JOCI Scalable & Reliable Capable of evaluating/training inference systems Label Distribution 101
102 Label Distribution is Balanced JOCI Likely Plausible Very-likely Technically possible Impossible 102
103 Label Distribution SNLI 103
104 Label Distribution SNLI Very-likely Entailment 104
105 Label Distribution SNLI Very-likely Plausible Entailment Neutral 105
106 Label Distribution SNLI Very-likely Plausible Techpossible Impossible Entailment Neutral Contradiction 106
107 Label Distribution ROCStories Impossible Technically possible Very-likely Likely Plausible 107
108 Our Goal for JOCI Scalable & Reliable Capable of evaluating/training inference systems Label Distribution 108
109 Our Goal for JOCI Scalable & Reliable Capable of evaluating/training inference systems Label Distribution Baselines 109
110 Our Goal for JOCI Scalable & Reliable Capable of evaluating/training inference systems Label Distribution Baselines Baseline(JOCI) > Baseline(SNLI/ROCStories) 110
111 Common Sense for language New task: Ordinal Common-sense Inference Common Sense from language Mining Common-sense is Challenging - Human Elicitation (Elicitation bias) - Text Mining (Reporting bias) Our Approach Text Mining + Crowdsourced Annotation New corpus: JOCI 111
112 JOCI Sheng Zhang Rachel Rudinger Kevin Duh Benjamin Van Durme 112
113 JOCI Thank you! Sheng Zhang Rachel Rudinger Kevin Duh Benjamin Van Durme 113
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