scholarly journals Not unreasonable: Why two negatives don't make a positive

2019 ◽  
Author(s):  
Michael Henry Tessler ◽  
Michael Franke
Keyword(s):  
Computational Model ◽  
Language Understanding ◽  
Black And White ◽  
Additional Prediction

Logic tells us that two negatives make a positive, but in language, things are not so black and white: A person "not unhappy" may not be entirely happy. We hypothesize that innovative uses of double negatives like "not unhappy" stem from listeners entertaining flexible meanings for negation markers like "not" and "un-", which context can then help disambiguate. We formalize this hypothesis in a computational model of language understanding, which predicts that "not unhappy" means something different from "happy" but also makes the additional prediction that single negations ("unhappy" vs. "not happy") are interpreted identically except when a speaker uses both in the same context, which we confirm experimentally. Even double negations that flagrantly use the same negation marker twice (e.g., "not not happy") are interpreted in subtle ways. These findings suggest that even one of the most logical elements of language—negation—can mean many things at once.

scholarly journals Incremental Understanding of Conjunctive Generic Sentences

2019 ◽  
Author(s):  
Michael Henry Tessler ◽  
Karen Gu ◽  
Roger Philip Levy
Keyword(s):  
Computational Model ◽  
Behavioral Experiments ◽  
Language Understanding ◽  
Incremental Processing ◽  
Generic Language ◽  
Processing Mechanism ◽  
Generic Sentences

Generic statements convey generalizations about categories, but how generic predications combine is unclear. “Elephants live in Africa and Asia” does not mean that individual elephants live on both continents. In addition, such conjunctive generics pose interesting questions for theories of incremental processing because the meaning of the sentence can change part-way through: “Elephants live in Africa” would imply most or all do, but “Africa and Asia” implies some live in each. We extend a recently proposed computational model of generic language understanding with an incremental processing mechanism that can begin to interpret an utterance before a speaker has finished their sentence. This model makes novel predictions about partial interpretations of conjunctive generic sentences, which we test in two behavioral experiments. The results support a strong view of incrementality, wherein listeners continuously update their beliefs based on expectations about where a speaker will go next with their utterance.

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