Predicting Pragmatic Reasoning in Language Games

By Michael C. Frank, Noah D. Goodman. 2012

975 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Experience Grounds Language (2020, background): “A.M. Turing (Computing Machinery and Intelligence 1950) In the language community, this paradigm has been described under the “Speaker-Listener” model (Stephens et al., 2010), and a rich theory to describe this computationally is being actively developed under the Rational Speech Act Model (Frank an”
GeDi: Generative Discriminator Guided Sequence Generation (2020, background): “GeDi also relates to the rational speech acts framework for computational pragmatics (Frank and Goodman, 2012; Goodman and Stuhlmüller, 2013) where a “listener” model and a “speaker” model interactively generate a sequence such that the listener can recover the input.”
The Hanabi Challenge: A New Frontier for AI Research (2019, background): “signiﬁcant ﬁnancial support for this work that could have inﬂuenced its outcome.”
Speaker-Follower Models for Vision-and-Language Navigation (2018, methodology): “explicit probabilistic reasoning combining speaker and follower agents is a staple of the literature on computational pragmatics [14], application of these models has largely been limited to extremely simple decision-making tasks like single forced choices.”
Learning Language Games through Interaction (2016, methodology): “To speed up learning, we leverage computational models of pragmatics (Jäger, 2008; Golland et al., 2010; Frank and Goodman, 2012; Smith et al., 2013; Vogel et al., 2013).”
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning (2018, background): “Second, it misses the conceptual connection between communication and reasoning over the beliefs of others, which is known to be important to how humans learn to communicate (Grice, 1975; Frank & Goodman, 2012).”
Machine Teaching for Bayesian Learners in the Exponential Family (2013, background): “en loss() and eort(), such as not-teaching-at-all in Example 4, or non-brute-force-teaching in Example 5. An interesting variant studied in cognitive science is when the learner expects to be taught [20, 8]. We defer the discussion on this variant, known as “collusion” in computational teaching theo”
TuringAdvice: A Generative and Dynamic Evaluation of Language Use (2021, methodology): “Our evaluation relates to pragmatics in NLP, where communication is modeled also through listeners and speakers (Golland et al., 2010; Frank and Goodman, 2012).”
Typical Decoding for Natural Language Generation (2022, background): “The rational speech act (RSA; Frank and Goodman, 2012) is a recently-proposed framework which models a speaker’s pragmatic behavior.”