Papers with language compositionality

2 papers
Convolutional Neural Networks with Recurrent Neural Filters (D18-1)

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Challenge: Convolutional neural networks (CNNs) use recurrent neural networks as convolution filters to capture language compositionality and long-term dependencies.
Approach: They propose to use recurrent neural networks (RNNs) as convolution filters to capture language compositionality and long-term dependencies.
Outcome: The proposed convolutional neural networks achieve state-of-the-art on two sentences and the Stanford Sentiment Treebank.
Disentangling Categorization in Multi-agent Emergent Communication (2022.naacl-main)

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Challenge: Recent work on the emergence of language between artificial agents has not isolated the effect of categorization power on inter-communication ability.
Approach: They propose to use disentangled representations to quantify categorization power of agents to enable differential analysis between combinations of heterogeneous systems.
Outcome: The proposed method reduces signaling accuracy by 40% despite encouraging compositionality in the artificial language.

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