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Found 9 papers, 2 papers with code
Fast visual grounding in interaction: bringing few-shot learning with neural networks to an interactive robot
The major shortcomings of using neural networks with situated agents are that in incremental interaction very few learning examples are available and that their visual sensory representations are quite different from image caption datasets.
Synthetic Propaganda Embeddings To Train A Linear Projection
This paper presents a method of detecting fine-grained categories of propaganda in text.
What goes into a word: generating image descriptions with top-down spatial knowledge
The aim of this paper is to evaluate what representations facilitate generating image descriptions with spatial relations and lead to better grounded language generation.
What a neural language model tells us about spatial relations
Understanding and generating spatial descriptions requires knowledge about what objects are related, their functional interactions, and where the objects are geometrically located.
Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection
We present and compare two alternative deep neural architectures to perform word-level metaphor detection on text: a bi-LSTM model and a new structure based on recursive feed-forward concatenation of the input.
Exploring the Functional and Geometric Bias of Spatial Relations Using Neural Language Models
The challenge for computational models of spatial descriptions for situated dialogue systems is the integration of information from different modalities.
``Deep'' Learning : Detecting Metaphoricity in Adjective-Noun Pairs
We show that using a single neural network combined with pre-trained vector embeddings can outperform the state of the art in terms of accuracy.
