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Found 6 papers, 3 papers with code
Language Controls More Than Top-Down Attention: Modulating Bottom-Up Visual Processing with Referring Expressions
How to best integrate linguistic and perceptual processing in multimodal tasks is an important open problem.
Modulating Bottom-Up and Top-Down Visual Processing via Language-Conditional Filters
Our experiments reveal that using language to control the filters for bottom-up visual processing in addition to top-down attention leads to better results on both tasks and achieves competitive performance.
Team Howard Beale at SemEval-2019 Task 4: Hyperpartisan News Detection with BERT
This paper describes our system for SemEval-2019 Task 4: Hyperpartisan News Detection (Kiesel et al., 2019).
Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations
Therefore, we further propose Bayesian learning to resolve such inconsistencies between the natural language grounding and a robot's world representation by exploiting spatio-relational information that is implicitly present in instructions given by a human.
A new dataset and model for learning to understand navigational instructions
Our goal is to develop a model that can learn to follow new instructions given prior instruction-perception-action examples.
CharNER: Character-Level Named Entity Recognition
We describe and evaluate a character-level tagger for language-independent Named Entity Recognition (NER).
