49 citations · 49 across the 4 of their papers we have counts for
4 papers
Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning
Angeliki Lazaridou, Anna Potapenko, Olivier Tieleman
We present a method for combining multi-agent communication and traditional data-driven approaches to natural language learning, with an end goal of teaching agents to communicate…
Compressive Transformers for Long-Range Sequence Modelling
Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar +1
We present the Compressive Transformer, an attentive sequence model which compresses past memories for long-range sequence learning. We find the Compressive Transformer obtains sta…
Learning and Evaluating Sparse Interpretable Sentence Embeddings
Valentin Trifonov, Octavian-Eugen Ganea, Anna Potapenko +1
Previous research on word embeddings has shown that sparse representations, which can be either learned on top of existing dense embeddings or obtained through model constraints du…
Interpretable probabilistic embeddings: bridging the gap between topic models and neural networks
Anna Potapenko, Artem Popov, Konstantin Vorontsov
We consider probabilistic topic models and more recent word embedding techniques from a perspective of learning hidden semantic representations. Inspired by a striking similarity o…