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Found 5 papers, 3 papers with code
Disentangled activations in deep networks
Deep neural networks have been tremendously successful in a number of tasks.
Disentangled Representations for Manipulation of Sentiment in Text
The ability to change arbitrary aspects of a text while leaving the core message intact could have a strong impact in fields like marketing and politics by enabling e. g. automatic optimization of message impact and personalized language adapted to the receiver's profile.
Query-Based Abstractive Summarization Using Neural Networks
In this paper, we present a model for generating summaries of text documents with respect to a query.
Learning to Play Guess Who? and Inventing a Grounded Language as a Consequence
The images from the game provide a non trivial environment for the agents to discuss and a natural grounding for the concepts they decide to encode in their communication.
Word Sense Disambiguation using a Bidirectional LSTM
In this paper we present a clean, yet effective, model for word sense disambiguation.
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