AMORE / AI and Language

Gemma BoledaUniversitat Pompeu Fabra

Frontier Research and Artifical IntelligenceERCEA conference

Brussels, October 25-26 2018

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Acknowledgements

This project has received funding from the European ResearchCouncil (ERC) under the European Union’s Horizon 2020 research

and innovation programme (grant agreement No 715154).

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AMOREA distributional MOdel of Reference to Entities

(ERC Starting Grant 715154)

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Language

“mug/cup” “I like it!”

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Symbolic approachesFormal semantics, logic-based Artificial Intelligence

“I like it!”

“Pass me the black cup, please.”

e1 e2 e3 e4

mug(e1)

mug(e2)

mouse(e3)

book(e4)

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Symbolic approachesFormal semantics, logic-based Artificial Intelligence

“I like it!”

“Pass me the black cup, please.”

e1 e2 e3 e4

mug(e1)

mug(e2)

mouse(e3)

book(e4)

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Symbolic approachesFormal semantics, logic-based Artificial Intelligence

“I like it!”

“Pass me the black cup, please.”

e1 e2 e3 e4

mug(e1)

mug(e2)

mouse(e3)

book(e4)

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Continuous approachesDistributional semantics, Neural Networks / deep learning

representation learning

“I like it!”

“Pass me the black cup, please.”

“Italy” / “this mug”

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Continuous approachesDistributional semantics, Neural Networks / deep learning

representation learning

“I like it!”

“Pass me the black cup, please.”

“Italy” / “this mug”

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Continuous approachesDistributional semantics, Neural Networks / deep learning

representation learning

“I like it!”

“Pass me the black cup, please.”

“Italy” / “this mug”

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Continuous approachesDistributional semantics, Neural Networks / deep learning

representation learning

“I like it!”

“Pass me the black cup, please.”

“Italy” / “this mug”

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Our proposalAMORE: A distributional MOdel of Reference to Entities (ERC StG 715154)

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AMORE: Key points

I model both discrete and fuzzy behaviorI learn from fewer examples

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Learning to refer: Character identificationSemEval 2018 Task 4

[JOEY]:". . . see Ross, because I think you love this woman ."

335 183 335 185

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Results

…

…

…

entities

…

…

…

SemEval 2018 competitionno entity library 26other model (2nd) 37with entity library (1st) 41

work in progressno entity library 34with entity library 53

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Results

…

…

…

entities

…

…

…

SemEval 2018 competitionno entity library 26other model (2nd) 37with entity library (1st) 41

work in progressno entity library 34with entity library 53

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Infrequent entities

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AI and Language

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AI and language: Potential

AI has advanced on its modeling of language by moving torepresentation learning

I learned from examples: radically empiricalI continuous distributed representationsI scalable

Huge advances in computational applications

I Machine Translation (Google translate)I Labeling objects in images (Facebook)I . . .

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AI and language: Potential

huge potential for scientific inquiry

I for instance: evidence that deep learning models learnhierarchical structure to some extent

→ will feed back to applications

I needs domain knowledge: Linguistics →multidisciplinarity→ same for Humanities / Social Sciences more broadly

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AI and language: Potential

huge potential for scientific inquiry

I for instance: evidence that deep learning models learnhierarchical structure to some extent

→ will feed back to applicationsI needs domain knowledge: Linguistics →multidisciplinarity→ same for Humanities / Social Sciences more broadly

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AI and language: Pitfall

Great! Study math and programming for three years and come back.

What you do is cool! I want to do it,

too!

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AI and language: Changes

make European education systems more flexible

I hard division into Scientific/Engineering and SocialScience/Humanities obsolete

I starting in high school,I easy to go from one to the otherI do both – major / minor?I strengthen quantitative training in traditional Social

Science/Humanities areas

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AI: Future

What we can do

/ what needs to happen

I create models that do one specific task quite well. . .

→ generalize: learn to carry out different tasksI translate and be able to point to the mug in a picture

I . . . if we provide them with many examplesI remember “Italy”?

→ learn from fewer examples

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AI: Future

What we can do / what needs to happen

I create models that do one specific task quite well. . .

→ generalize: learn to carry out different tasksI translate and be able to point to the mug in a picture

I . . . if we provide them with many examplesI remember “Italy”?

→ learn from fewer examples

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AI: Future

What we can do / what needs to happen

I create models that do one specific task quite well. . .→ generalize: learn to carry out different tasks

I translate and be able to point to the mug in a picture

I . . . if we provide them with many examplesI remember “Italy”?

→ learn from fewer examples

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AI: Future

What we can do / what needs to happen

I create models that do one specific task quite well. . .→ generalize: learn to carry out different tasks

I translate and be able to point to the mug in a picture

I . . . if we provide them with many examplesI remember “Italy”?

→ learn from fewer examples

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AI: Future

generalize to different tasks and learn from fewerexamples

I reuse knowledgeI work at a higher level of abstractionI move from representation learning to model learning?

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AMORE / AI and Language

Gemma BoledaUniversitat Pompeu Fabra

Frontier Research and Artifical IntelligenceERCEA conference

Brussels, October 25-26 2018

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