A Paradigm for Situated and Goal-Driven Language Learning

By Jon Gauthier, Igor Mordatch. 2016

32 citations recorded by the source corpus.

A distinguishing property of human intelligence is the ability to flexibly use language in order to communicate complex ideas with other humans in a variety of contexts. Research in natural language dialogue should focus on designing communicative agents which can integrate themselves into these contexts and productively collaborate with humans. In this abstract, we propose a general situated language learning paradigm which is designed to bring about robust language agents able to cooperate productively with humans.

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Citations in the corpus

Learning to Speak and Act in a Fantasy Text Adventure Game (2019, background): “Several position papers have proposed virtual embodiment as a strategy for language research (Brooks, 1991; Kiela et al., 2016; Gauthier and Mordatch, 2016; Mikolov et al., 2016; Lake et al., 2017).”
Are Distributional Representations Ready for the Real World? Evaluating Word Vectors for Grounded Perceptual Meaning (2017, positive): “Despite the success of distributional representations in standard natural language processing tasks, a small but growing consensus within the artificial intelligence community suggests that these methods cannot be sufficient to induce adequate representations of words and concepts (Kiela et al., 201”
Translating Neuralese (2017, background): “In probabilistic terms, this says that the meaning of a message z a is represented by the distribution p ( x a | z a , x b ) it induces over speaker states.”
How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds (2020, background): “One possible reason for this is that many datasets and tasks used for NLP are static, not supporting interaction and language grounding (Brooks, 1991; Feldman and Narayanan, 2004; Barsalou, 2008; Mikolov et al., 2016; Gauthier and Mordatch, 2016; Lake et al., 2017).”