Commonsense resources - University of Washingtonmsap/acl2020...Overview of existing resources...
Transcript of Commonsense resources - University of Washingtonmsap/acl2020...Overview of existing resources...
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Commonsense resources
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Tom’s grandma was reading a new book, when she dropped her glasses.
She couldn’t pick them up, so she called Tom for help.
Tom rushed to help her look for them, they heard a loud crack.
They realized that Tom broke her glasses by stepping on them.
Promptly, his grandma yelled at Tom to go get her a new pair.
Grandma’s glasses
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Humans reason about the world with mental models [Graesser, 1994]
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Humans reason about the world with mental models [Graesser, 1994]
Personal experiences[Conway et al., 2000]
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Humans reason about the world with mental models [Graesser, 1994]
Personal experiences[Conway et al., 2000]
World knowledge and commonsense[Kintsch, 1988]
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Humans reason about the world with mental models [Graesser, 1994]
Personal experiences[Conway et al., 2000]
World knowledge and commonsense[Kintsch, 1988]
Commonsense resources aim to be a bank of knowledge for machines to be able to reason
about the world in tasks
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Tom’s grandma was reading a new book, when she dropped her glasses.
She couldn’t pick them up, so she called Tom for help.
Tom rushed to help her look for them, they heard a loud crack.
They realized that Tom broke her glasses by stepping on them.
Promptly, his grandma yelled at Tom to go get her a new pair.
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Tom’s grandma was reading a new book, when she dropped her glasses.
She couldn’t pick them up, so she called Tom for help.
Tom rushed to help her look for them, they heard a loud crack.
They realized that Tom broke her glasses by stepping on them.
Promptly, his grandma yelled at Tom to go get her a new pair.
usedFor
Y will want
Y will
ConceptNet
ATOMIC
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Tom’s grandma was reading a new book, when she dropped her glasses.
She couldn’t pick them up, so she called Tom for help.
Tom rushed to help her look for them, they heard a loud crack.
They realized that Tom broke her glasses by stepping on them.
Promptly, his grandma yelled at Tom to go get her a new pair.
usedFor
Y will want
Y will
capableOf
improve ones vision
people
relaxing
subeventOf
activity
typeOf
usedFor
X feels
nervous
corrective lens
typeOf
X wanted to
express anger
ConceptNet
ATOMIC
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Overview of existing resources
Cyc(Lenat et al., 1984)
today
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Overview of existing resources
OpenCyc(Lenat, 2004)
Cyc(Lenat et al., 1984)
OpenCyc 4.0(Lenat, 2012)
ResearchCyc(Lenat, 2006)
today
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Overview of existing resources
OpenCyc(Lenat, 2004)
Open Mind Common Sense(Minsky, Singh & Havasi, 1999)
Cyc(Lenat et al., 1984)
OpenCyc 4.0(Lenat, 2012)
ResearchCyc(Lenat, 2006)
today
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Overview of existing resources
OpenCyc(Lenat, 2004)
ConceptNet 5.5(Speer et al., 2017)
ConceptNet(Liu & Singh, 2004)
Open Mind Common Sense(Minsky, Singh & Havasi, 1999)
Cyc(Lenat et al., 1984)
OpenCyc 4.0(Lenat, 2012)
ResearchCyc(Lenat, 2006)
today
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Overview of existing resources
OpenCyc(Lenat, 2004)
ConceptNet 5.5(Speer et al., 2017)
Web Child 2.0(Tandon et al., 2017)
ConceptNet(Liu & Singh, 2004)
Web Child(Tandon et al., 2014)
Open Mind Common Sense(Minsky, Singh & Havasi, 1999)
Cyc(Lenat et al., 1984)
OpenCyc 4.0(Lenat, 2012)
ResearchCyc(Lenat, 2006)
NELL(Mitchell et al., 2015)
NELL(Carlson et al., 2010)
today
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Overview of existing resourcesATOMIC
(Sap et al., 2019)
OpenCyc(Lenat, 2004)
ConceptNet 5.5(Speer et al., 2017)
Web Child 2.0(Tandon et al., 2017)
ConceptNet(Liu & Singh, 2004)
Web Child(Tandon et al., 2014)
Open Mind Common Sense(Minsky, Singh & Havasi, 1999)
Cyc(Lenat et al., 1984)
OpenCyc 4.0(Lenat, 2012)
ResearchCyc(Lenat, 2006)
NELL(Mitchell et al., 2015)
NELL(Carlson et al., 2010)
today
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How do you create a commonsense resource?
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Desiderata for a good commonsense resource
Coverage
• Large scale
• Diverse knowledge types
Useful
• High quality knowledge
• Usable in downstream tasks
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Desiderata for a good commonsense resource
Coverage
• Large scale
• Diverse knowledge types
Useful
• High quality knowledge
• Usable in downstream tasks
Multiple resources tackle different knowledge types
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Creating a commonsense resource
Symbolic
Natural languageRepresentation
Knowledge type Semantic
Inferential
Domain-specific
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What is ConceptNet?
• General commonsense knowledge
• 21 million edges and over 8 million nodes (as of 2017)• Over 85 languages
• In English: over 1.5 million nodes
• Knowledge covered:• Open Mind Commonsense assertions
• Wikipedia/Wiktionary semantic knowledge
• WordNet, Cyc ontological knowledge
http://conceptnet.io/
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ATOMIC:inferential knowledge in natural language form
https://mosaickg.apps.allenai.org/kg_atomic
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ATOMIC: 880,000 triples for AI systems to reason
about causes and effects of everyday situations
X repelsY’s attack
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X repelsY’s attack
nine inference dimensions
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Causes
X repelsY’s attack
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Effects
X repelsY’s attack
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Dynamic
X repelsY’s attack
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Static
X repelsY’s attack
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Voluntary
X repelsY’s attack
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Involuntary
X repelsY’s attack
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AgentX repelsY’s attack
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ThemeX repelsY’s attack
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X repelsY’s attack
300,000 event nodes to date
880,000 if-Event-then-* knowledge triples
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ATOMIC: knowledge of cause and effect
• Humans have theory of mind, allowing us to• make inferences about people’s mental states
• understand likely events that precede and follow (Moore, 2013)
Theory of Mind
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ATOMIC: knowledge of cause and effect
• Humans have theory of mind, allowing us to• make inferences about people’s mental states
• understand likely events that precede and follow (Moore, 2013)
• AI systems struggle with inferential reasoning• only find complex correlational patterns in data
• limited to the domain they are trained on(Pearl; Davis and Marcus 2015; Lake et al. 2017; Marcus 2018)
Theory of Mind
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Overview of existing resourcesATOMIC
(Sap et al., 2019)
OpenCyc(Lenat, 2004)
ConceptNet 5.5(Speer et al., 2017)
Web Child 2.0(Tandon et al., 2017)
ConceptNet(Liu & Singh, 2004)
Web Child(Tandon et al., 2014)
Open Mind Common Sense
(Singh, 2002)
Cyc(Lenat et al., 1984)
OpenCyc 4.0(Lenat, 2012)
ResearchCyc(Lenat, 2006)
NELL(Mitchell et al., 2015)
NELL(Carlson et al., 2010)
today
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Existing knowledge bases
ATOMIC(Sap et al., 2019)
ConceptNet 5.5(Speer et al., 2017)
OpenCyc 4.0(Lenat, 2012)
NELL(Mitchell et al., 2015)
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Existing knowledge bases
Represented in symbolic logic(e.g., LISP-style logic)
Represented in natural language(how humans talk and think)
ATOMIC(Sap et al., 2019)
ConceptNet 5.5(Speer et al., 2017)
OpenCyc 4.0(Lenat, 2012)
NELL(Mitchell et al., 2015)
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Existing knowledge bases
Represented in symbolic logic(e.g., LISP-style logic)
Represented in natural language(how humans talk and think)
ATOMIC(Sap et al., 2019)
ConceptNet 5.5(Speer et al., 2017)
OpenCyc 4.0(Lenat, 2012)
NELL(Mitchell et al., 2015)
(#$implies (#$and (#$isa ?OBJ ?SUBSET)(#$genls ?SUBSET ?SUPERSET))
(#$isa ?OBJ ?SUPERSET))
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Existing knowledge bases
Represented in symbolic logic(e.g., LISP-style logic)
Represented in natural language(how humans talk and think)
Knowledge of “what”(taxonomic: A isA B)
Knowledge of “why” and “how”(inferential: causes and effects)
ATOMIC(Sap et al., 2019)
ConceptNet 5.5(Speer et al., 2017)
OpenCyc 4.0(Lenat, 2012)
NELL(Mitchell et al., 2015)
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Q: How do you gather commonsense knowledge at scale?
A: It depends on the type of knowledge
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Extracting commonsense from textBased on information extraction (IE) methods
1. Read and parse text
2. Create candidate rules
3. Filter rules based on quality metric
Advantage:can extract knowledge automatically
Example system: Never Ending Language Learner (NELL; Carlson et al., 2010)
… more on this later with temporal commonsense
isA(senator,Brownback)location(Kansas,Brownback)isA(senator,Kansas)...
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Some commonsense cannot be extracted
Text is subject to reporting bias(Gordon & Van Durme, 2013)
• Idioms & figurative usage“Black sheep problem”
• Noteworthy events Murdering 4x more common than exhaling
Commonsense is not often written-> Grice’s maxim of quantity
found when extracting commonsense knowledge on four large corpora using
Knext (Gordon & Van Durme, 2013)
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Eliciting commonsense from humans
Experts create knowledge base
• Advantages:• Quality guaranteed
• Can use complex representations(e.g., CycL, LISP)
• Drawbacks:• Time cost
• Training users
WordNet(Miller et al., 1990)
OpenCyc 4.0(Lenat, 2012)
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Eliciting commonsense from humans
Experts create knowledge base
• Advantages:• Quality guaranteed
• Can use complex representations(e.g., CycL, LISP)
• Drawbacks:• Time cost
• Training users
Non-experts write knowledge in natural language phrases
• Natural language• Accessible to non-experts
• Different phrasings allow for more nuanced knowledge
• Fast and scalable collection• Crowdsourcing
• Games with a purpose
ATOMIC(Sap et al., 2019)
ConceptNet 5.5(Speer et al., 2017)
WordNet(Miller et al., 1990)
OpenCyc 4.0(Lenat, 2012)
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Eliciting commonsense from humans
Experts create knowledge base
• Advantages:• Quality guaranteed
• Can use complex representations(e.g., CycL, LISP)
• Drawbacks:• Time cost
• Training users
Non-experts write knowledge in natural language phrases
• Natural language• Accessible to non-experts
• Different phrasings allow for more nuanced knowledge
• Fast and scalable collection• Crowdsourcing
• Games with a purpose
ATOMIC(Sap et al., 2019)
ConceptNet 5.5(Speer et al., 2017)
WordNet(Miller et al., 1990)
OpenCyc 4.0(Lenat, 2012)
![Page 55: Commonsense resources - University of Washingtonmsap/acl2020...Overview of existing resources OpenCyc (Lenat, 2004) ConceptNet 5.5 (Speer et al., 2017) Web Child 2.0 (Tandon et al.,](https://reader034.fdocuments.in/reader034/viewer/2022052101/603b166554fdef4cd065921c/html5/thumbnails/55.jpg)
Knowledge bases and mitigating biases
• Different data collection methods suffer from social biases differently
• ConceptNet word embeddings have less demographic biases than GloVe embeddings [Sweeney & Najafian, 2019]
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Knowledge bases and mitigating biases
PersonX clutches a gunbecause X wanted to
ATOMIC (Sap et al., 2019)
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Knowledge bases and mitigating biases
Karen clutches a gun because X wanted to
Jaquain clutches a gun because X wanted to
PersonX clutches a gunbecause X wanted to
COMET (Bosselut et al., 2019): ATOMIC + OpenAI GPT
ATOMIC (Sap et al., 2019)
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What’s next with commonsense resources?
• Use them with models in downstream tasks• Reading comprehension, QA tasks, etc.
• Create inference or reasoning engines• Knowledge base construction, multi-hop reasoning, etc.
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Tom’s grandma was reading a new book, when she dropped her glasses.
She couldn’t pick them up, so she called Tom for help.
Tom rushed to help her look for them, they heard a loud crack.
They realized that Tom broke her glasses by stepping on them.
Promptly, his grandma yelled at Tom to go get her a new pair.
usedFor
Y will want
Y will
capableOf
improve ones vision
people
relaxing
subeventOf
activity
typeOf
usedFor
X feels
nervous
corrective lens
typeOf
X wanted to
express anger
ConceptNet
ATOMIC
Thanks! Questions?