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Quantifying social biases in NLP: A generalization and empirical comparison of extrinsic fairness metrics
Measuring bias is key for better understanding and addressing unfairness in NLP/ML
models. This is often done via fairness metrics, which quantify the differences in a model's …
models. This is often done via fairness metrics, which quantify the differences in a model's …
Anti-efficient encoding in emergent communication
Despite renewed interest in emergent language simulations with neural networks, little is
known about the basic properties of the induced code, and how they compare to human …
known about the basic properties of the induced code, and how they compare to human …
A survey on emergent language
The field of emergent language represents a novel area of research within the domain of
artificial intelligence, particularly within the context of multi-agent reinforcement learning …
artificial intelligence, particularly within the context of multi-agent reinforcement learning …
Toward More Human-Like AI Communication: A Review of Emergent Communication Research
In the recent shift towards human-centric AI, the need for machines to accurately use natural
language has become increasingly important. While a common approach to achieve this is …
language has become increasingly important. While a common approach to achieve this is …
EGG: a toolkit for research on Emergence of lanGuage in Games
There is renewed interest in simulating language emergence among deep neural agents
that communicate to jointly solve a task, spurred by the practical aim to develop language …
that communicate to jointly solve a task, spurred by the practical aim to develop language …
Capacity, bandwidth, and compositionality in emergent language learning
Many recent works have discussed the propensity, or lack thereof, for emergent languages
to exhibit properties of natural languages. A favorite in the literature is learning …
to exhibit properties of natural languages. A favorite in the literature is learning …
What they do when in doubt: a study of inductive biases in seq2seq learners
Sequence-to-sequence (seq2seq) learners are widely used, but we still have only limited
knowledge about what inductive biases shape the way they generalize. We address that by …
knowledge about what inductive biases shape the way they generalize. We address that by …
Co-evolution of language and agents in referential games
Referential games offer a grounded learning environment for neural agents which accounts
for the fact that language is functionally used to communicate. However, they do not take into …
for the fact that language is functionally used to communicate. However, they do not take into …
The curious case of representational alignment: Unravelling visio-linguistic tasks in emergent communication
Natural language has the universal properties of being compositional and grounded in
reality. The emergence of linguistic properties is often investigated through simulations of …
reality. The emergence of linguistic properties is often investigated through simulations of …
Nellcom-x: A comprehensive neural-agent framework to simulate language learning and group communication
Recent advances in computational linguistics include simulating the emergence of human-
like languages with interacting neural network agents, starting from sets of random symbols …
like languages with interacting neural network agents, starting from sets of random symbols …