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Some of the Team Facebook AI Intelligent Dialogue tests Tests 5 - - PowerPoint PPT Presentation
Some of the Team Facebook AI Intelligent Dialogue tests Tests 5 - - PowerPoint PPT Presentation
Some of the Team Facebook AI Intelligent Dialogue tests Tests 5 99.5% represents an error in the test result due to an error in the input file. We did not attempt to recode to produce the wrong result to pass the test Demo 1: Meaning Matcher
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Some of the Team
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Tests 5 99.5% represents an error in the test result due to an error in the input file. We did not attempt to recode to produce the wrong result to pass the test
Facebook AI Intelligent Dialogue tests
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Demo 1: Meaning Matcher
- “The shark bit the surfer at the beach yesterday because it was hungry.”
- “The shark bit the surfer the dog kissed at the beach yesterday because it was
hungry.”
- “The shark bit no ate the surfer the dog kissed at the beach yesterday because
it was hungry.”
- “Can you push the door open?”
- “Can’t you push the door open?”
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Natural Language Processing (NLP)
Text In Text Out Deep Learning / ANN / Computational
- NLU was abandoned as a goal
in the 1950s and 1960s
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Science / Ockham’s Razor Select the hypothesis with the fewest assumptions
Aristotle (384–322 BC) “Nature operates in the shortest way possible” John Duns Scotus (1265–1308) “Pluralitas non est ponenda sine necessitate (Plurality is not to be
posited without necessity)”
Bertrand Russell (1872-1970) “Whenever possible, substitute constructions out of known entities for
inferences to unknown entities”
Albert Einstein (1879-1955) “A scientific theory should be as simple as possible, but no simpler”
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Linking Algorithm
Role and Reference Grammar (RRG)
Syntactic representation Semantic representation Discourse-pragmatics
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Scientific Models for Linguistics
You shall know a word by the company it keeps (Firth, 1957)
Chomsky 1957
Study Syntax “Rules- based”
J.R. Firth 1957
Social / Context
Van Valin 1980s
Study Language & Meaning in Context
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Many NLP Tools – eg. Stanford
- Tokenizer
- Temporal tagger
- Parser
- Named Entity Recognition (eg. PERSON, ORGANIZATION, LOCATION)
Stanford’s Dan Jurafsky c.2011
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This science doesn’t support engineering
Noam Chomsky, Syntactic Structures, 1957 “The fundamental aim in the linguistic analysis of a language L is to separate the grammatical sequences which are the sentences of L from the ungrammatical sequences which are not sentences of L and to study the structure of the grammatical sequences.” … “Sentences (1) and (2) are equally nonsensical, but any speaker of English will recognize that only the former is grammatical.
- (1) Colorless green ideas sleep furiously.
- (2) Furiously sleep ideas green colorless.”
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Structure is both grammatical and ungrammatical
Colorless green ideas sleep furiously JJ JJ NNS VBP RB adj adj noun pl verb 3sg pres adverb Running running runnings run furiously Red little ridinghoods run furiously Little red ridinghoods run furiously Small round birds eat hungrily
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Better to look at meaning – predicates resolve
Predicate/ Referent/ Modifier (NOT noun, verb, adverb, adjective, etc) The running men (the men are running/the running of men) do'(men, [run'(men)]) The running water (the water is running/the running of water) do’(water, [flow'(water)]) The running men are tired The running water is cold The tired running men ran the cold running water
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Category Squishes
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Inferring mathematics
- Language understanding (NLU) with patterns (not templates or symbols)
- The cat sadly ate the rat on the mat slowly yesterday
- (cat) – (eat) – (rat) + where (on the mat) + when (yesterday)
- 1+1=10
- (number) + (number) = (number)
- 1+1=2
- (number) + (number) = (number)
- “Can you start a song?” “Can you park in the back?”
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RRG Layered Structure
Nucleus Core Clause Sentence Push the door open (nuclear juncture) Promise Sandy to wash the car (core juncture) Tomorrow (when) Persuade? In the car (where) Evidently (evidential) In an hour (when) e.g. I went because I was hungry to the burger place
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Demo 2/3: bAbI (benchmark) & conversation
- Facebook’s AI Research Team (FAIR) directed by Yann LeCunn (Deep
Learning scientist). Our report published at arXive (per FAIR): https://arxiv.org/
- HAVE’
- Results improve on keyword capacity of world’s best (distributional hypothesis)
- The fish is off. The light is off. The party is off.
- Call Beth, no John. Who do you call?
- Translation: “The dog bit the mother”
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