9 citations · 10 across the 3 of their papers we have counts for
4 papers · 1 filter
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…
Interpretable agent communication from scratch (with a generic visual processor emerging on the side)
Roberto Dessì, Eugene Kharitonov, Marco Baroni
As deep networks begin to be deployed as autonomous agents, the issue of how they can communicate with each other becomes important. Here, we train two deep nets from scratch to pe…
Focus on What's Informative and Ignore What's not: Communication Strategies in a Referential Game
Roberto Dessì, Diane Bouchacourt, Davide Crepaldi +1
Research in multi-agent cooperation has shown that artificial agents are able to learn to play a simple referential game while developing a shared lexicon. This lexicon is not easy…
CNNs found to jump around more skillfully than RNNs: Compositional generalization in seq2seq convolutional networks
Roberto Dessì, Marco Baroni
Lake and Baroni (2018) introduced the SCAN dataset probing the ability of seq2seq models to capture compositional generalizations, such as inferring the meaning of "jump around" 0-…