8 papers
Can Transformers Jump Around Right in Natural Language? Assessing Performance Transfer from SCAN
Rahma Chaabouni, Roberto Dessì, Eugene Kharitonov
Despite their practical success, modern seq2seq architectures are unable to generalize systematically on several SCAN tasks. Hence, it is not clear if SCAN-style compositional gene…
"LazImpa": Lazy and Impatient neural agents learn to communicate efficiently
Mathieu Rita, Rahma Chaabouni, Emmanuel Dupoux
Previous work has shown that artificial neural agents naturally develop surprisingly non-efficient codes. This is illustrated by the fact that in a referential game involving a spe…
What they do when in doubt: a study of inductive biases in seq2seq learners
Eugene Kharitonov, Rahma Chaabouni
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 inve…
Compositionality and Generalization in Emergent Languages
Rahma Chaabouni, Eugene Kharitonov, Diane Bouchacourt +2
Natural language allows us to refer to novel composite concepts by combining expressions denoting their parts according to systematic rules, a property known as \emph{compositional…
EGG: a toolkit for research on Emergence of lanGuage in Games
Eugene Kharitonov, Rahma Chaabouni, Diane Bouchacourt +1
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-enabl…
Word-order biases in deep-agent emergent communication
Rahma Chaabouni, Eugene Kharitonov, Alessandro Lazaric +2
Sequence-processing neural networks led to remarkable progress on many NLP tasks. As a consequence, there has been increasing interest in understanding to what extent they process…