Pragmatic Language Interpretation as Probabilistic Inference

Goodman, Noah D.; Frank, Michael C.

doi:10.1016/j.tics.2016.08.005

Cited by 508 publications

(507 citation statements)

References 39 publications

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“…Both classic theories of 91 communication (e.g., Sperber & Wilson, 1995) and more recent probabilistic models of 92 pragmatic inference (e.g., Frank & Goodman, 2012; see Goodman & Frank, 2016 for review) 93 describe the processes that language users use to compute such implicatures in different ways.…”

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confidence: 99%

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The role of salience in young children's processing of ad-hoc implicatures

Yoon¹,

Frank²

2016

Preprint

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Language comprehension often requires making implicatures. For example, inferring that "I ate some of the cookies" implicates the speaker ate some but not all (scalar implicatures); and "I ate the chocolate-chip cookies" where there are both chocolate chip cookies and raisin cookies in the context implicates that the speaker ate the chocolate chip, but not both the chocolate chip and raisin cookies (ad-hoc implicatures). Children’s ability to make scalar implicatures develops around age five, with ad-hoc implicatures emerging somewhat earlier. In the current work, using a time-sensitive tablet paradigm, we examined developmental gains in children’s ad-hoc implicature processing, and found evidence for successful pragmatic inferences by children as young as 3 years in a supportive context and substantial developmental gains in inference computation from 2 to 5 years. We also tested whether one cause of younger children (2-year-olds)'s consistent failure to make pragmatic inferences is their difficulty in inhibiting an alternative interpretation that is more salient than the target meaning (the salience hypothesis). Our findings supported this hypothesis: Younger children’s failures with pragmatic inferences were related to effects of the salience mismatch between possible interpretations.

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confidence: 99%

“…Despite that, it is 100 useful to review the probabilistic view as it helps guide some of our predictions below. We 101 consider sentence (2), following the analysis given in Goodman and Frank (2016). Under the 102 rational speech act (RSA) model, there is a space of meanings (e.g., ATE(chocolate chip & 103 raisin), ATE(chocolate chip), etc.…”

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confidence: 99%

The role of salience in young children's processing of ad-hoc implicatures

Yoon¹,

Frank²

2016

Preprint

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“…In Cangelosi and Parisi (2004), the emergence of verb-noun separation is learned while the agents are interacting and manipulating the objects. Meanwhile, the tasks during of such interaction may be essential during learning too (Goodman and Frank 2016). Recent experiments (Rohlfing 2016;Andreas and Klein 2016) and also proposed that language learning should be posited in the context of task-directed behaviours.…”

Section: Language Understanding For Robot Systemsmentioning

confidence: 99%

Sensorimotor input as a language generalisation tool: a neurorobotics model for generation and generalisation of noun-verb combinations with sensorimotor inputs

et al. 2018

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The paper presents a neurorobotics cognitive model explaining the understanding and generalisation of nouns and verbs combinations when a vocal command consisting of a verb-noun sentence is provided to a humanoid robot. The dataset used for training was obtained from object manipulation tasks with a humanoid robot platform; it includes 9 motor actions and 9 objects placing placed in 6 different locations), which enables the robot to learn to handle real-world objects and actions. Based on the multiple time-scale recurrent neural networks, this study demonstrates its generalisation capability using a large data-set, with which the robot was able to generalise semantic representation of novel combinations of noun-verb sentences, and therefore produce the corresponding motor behaviours. This generalisation process is done via the grounding process: different objects are being interacted, and associated, with different motor behaviours, following a learning approach inspired by developmental language acquisition in infants. Further analyses of the learned network dynamics and representations also demonstrate how the generalisation is possible via the exploitation of this functional hierarchical recurrent network.

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“…To capture and independently manipulate the contributions of each of these factors, Savinelli et al (2017) modeled ambiguity resolution for every-not utterances within the Bayesian RSA framework (Goodman and Frank, 2016). They found that when it comes to understanding nonadult-like behavior in the TVJT, there is likely a stronger role for the pragmatics of context management (as realized in prior beliefs about world state and QUD) than for grammatical processing (as realized in the prior on scope interpretations), although there may be a role for both.…”

Section: Previous Work: Modeling Every-notmentioning

confidence: 99%

Exactly two things to learn from modeling scope ambiguity resolution: Developmental continuity and numeral semantics

Savinelli¹,

Scontras²,

Pearl³

2018

Proceedings of the 8th Workshop on Cognitive Modeling And Computational Linguistics (CMCL 2018)

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Behavioral data suggest that both children and adults struggle to access the inverse interpretation of scopally-ambiguous utterances in certain contexts. To determine whether the causes of both child and adult difficulty are similar, we extend an existing computational model of children's scope ambiguity resolution in context. We find that the same utterancedisambiguation mechanism is active in both children and adults, supporting the theory of developmental continuity. Moreover, because adult behavior requires an exact semantics for numerals, we also provide empirical support for this theory of linguistic representation.

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Pragmatic Language Interpretation as Probabilistic Inference

Cited by 508 publications

References 39 publications

The role of salience in young children's processing of ad-hoc implicatures

The role of salience in young children's processing of ad-hoc implicatures

Sensorimotor input as a language generalisation tool: a neurorobotics model for generation and generalisation of noun-verb combinations with sensorimotor inputs

Exactly two things to learn from modeling scope ambiguity resolution: Developmental continuity and numeral semantics

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