text: 353,132,1257354626912980992,46165258,Shane Steinert-Threlkeld,"New paper for #acl2020nlp! ""On the Spontaneous Emergence of Discrete and Compositional Signals"" with @nurikolan and Emmanuel Chemla Paper: <LINK> Code: <LINK> <LINK> We look at two of Hockett's design features for language---discreteness and displacement---in a general setting for emergent communication, where our message space is continuous instead of discrete. <LINK> This allows us to (i) explore when discrete signals emerge and (ii) avoid using tools like reinforcement learning, which are difficult in practice. We reliably find discreteness, in both production and preception. Two probes for compositional structure (vector analogies, and a composition prediction network) both fail. There are interesting and very robust patterns during training though (see original tweet's gif), so stay tuned! We also show how the rudiments of Categorical Perception---where discriminability in a continuous space depends on discrete labels, not merely perceptual distance---can be found in our message space. More ""psycholinguistic"" work in emergent communication would be great! <LINK>",https://arxiv.org/abs/2005.00110,"We propose a general framework to study language emergence through signaling games with neural agents. Using a continuous latent space, we are able to (i) train using backpropagation, (ii) show that discrete messages nonetheless naturally emerge. We explore whether categorical perception effects follow and show that the messages are not compositional. ",On the Spontaneous Emergence of Discrete and Compositional Signals,5,"['New paper for #acl2020nlp!\n\n""On the Spontaneous Emergence of Discrete and Compositional Signals"" \nwith @nurikolan and Emmanuel Chemla\n\nPaper: <LINK>\nCode: <LINK> <LINK>', ""We look at two of Hockett's design features for language---discreteness and displacement---in a general setting for emergent communication, where our message space is continuous instead of discrete. https://t.co/aHlwHAVLuQ"", 'This allows us to (i) explore when discrete signals emerge and (ii) avoid using tools like reinforcement learning, which are difficult in practice.  We reliably find discreteness, in both production and preception.', ""Two probes for compositional structure (vector analogies, and a composition prediction network) both fail.  There are interesting and very robust patterns during training though (see original tweet's gif), so stay tuned!"", 'We also show how the rudiments of Categorical Perception---where discriminability in a continuous space depends on discrete labels, not merely perceptual distance---can be found in our message space.  More ""psycholinguistic"" work in emergent communication would be great! https://t.co/tioN51gsVE']",20,05,1083