Emmanuel Chemla


Minimum Description Length Recurrent Neural Networks
Nur Lan | Michal Geyer | Emmanuel Chemla | Roni Katzir
Transactions of the Association for Computational Linguistics, Volume 10

We train neural networks to optimize a Minimum Description Length score, that is, to balance between the complexity of the network and its accuracy at a task. We show that networks optimizing this objective function master tasks involving memory challenges and go beyond context-free languages. These learners master languages such as anbn, anbncn, anb2n, anbmcn +m, and they perform addition. Moreover, they often do so with 100% accuracy. The networks are small, and their inner workings are transparent. We thus provide formal proofs that their perfect accuracy holds not only on a given test set, but for any input sequence. To our knowledge, no other connectionist model has been shown to capture the underlying grammars for these languages in full generality.


On the Spontaneous Emergence of Discrete and Compositional Signals
Nur Geffen Lan | Emmanuel Chemla | Shane Steinert-Threlkeld
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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.


Learning simulation of nominal/verbal contexts through n-grams (Simulation de l’apprentissage des contextes nominaux/verbaux par n-grammes) [in French]
Perrine Brusini | Pascal Amsili | Emmanuel Chemla | Anne Christophe
Proceedings of TALN 2014 (Volume 2: Short Papers)