293 research outputs found
Structural Inductive Biases in Emergent Communication
In order to communicate, humans flatten a complex representation of ideas and
their attributes into a single word or a sentence. We investigate the impact of
representation learning in artificial agents by developing graph referential
games. We empirically show that agents parametrized by graph neural networks
develop a more compositional language compared to bag-of-words and sequence
models, which allows them to systematically generalize to new combinations of
familiar features.Comment: The first two authors contributed equally. Poster presented at CogSci
202
Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog
A number of recent works have proposed techniques for end-to-end learning of
communication protocols among cooperative multi-agent populations, and have
simultaneously found the emergence of grounded human-interpretable language in
the protocols developed by the agents, all learned without any human
supervision!
In this paper, using a Task and Tell reference game between two agents as a
testbed, we present a sequence of 'negative' results culminating in a
'positive' one -- showing that while most agent-invented languages are
effective (i.e. achieve near-perfect task rewards), they are decidedly not
interpretable or compositional.
In essence, we find that natural language does not emerge 'naturally',
despite the semblance of ease of natural-language-emergence that one may gather
from recent literature. We discuss how it is possible to coax the invented
languages to become more and more human-like and compositional by increasing
restrictions on how two agents may communicate.Comment: 9 pages, 7 figures, 2 tables, accepted at EMNLP 2017 as short pape
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