3260 papers • 126 benchmarks • 313 datasets
Detecting predicates in sentences. Semantic frames are defined with respect to predicates. This task is a prerequisite to semantic role labeling.
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RWFNs are used to perform Visual Relationship Detection tasks, which are more challenging SII tasks and show that RWFNs outperform LTNs for the predicate-detection task while using fewer number of adaptable parameters (1:56 ratio).
LISA is a neural network model that combines multi-head self-attention with multi-task learning across dependency parsing, part-of-speech tagging, predicate detection and SRL, and can incorporate syntax using merely raw tokens as input.
Target-Aware Weighted Training is introduced, a weighted training algorithm for cross-task learning based on minimizing a representation-based task distance between the source and target tasks that allows one to reason in a theoretically principled way about several critical aspects of cross- task learning.
An unsupervised and training-free method for text compression, leading to a significant improvement on the previous perplexity-based method, and the high scalability of the method enables NDD to outperform the supervised state-of-the-art in domain adaption by a huge margin.
ND-based algorithm based on Neighboring Distribution (NDD) forms previous perplexity-based unsupervised algorithm by a large margin and is exploited for extractive sentence compression.
A novel self-supervised approach for representation learning, particularly for the task of Visual Relationship Detection (VRD), which is able to surpass state-of-the-art VRD methods on the Predicate Detection (PredDet) evaluation setting, using only a few annotated samples.
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