Learning Semantic Representations
12 papers with code • 0 benchmarks • 1 datasets
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Edge-Map-345C
Latest papers
Learning Semantic Representations for Unsupervised Domain Adaptation
Mid-Push/Moving-Semantic-Transfer-Network
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ICML 2018
Prior domain adaptation methods address this problem through aligning the global distribution statistics between source domain and target domain, but a drawback of prior methods is that they ignore the semantic information contained in samples, e. g., features of backpacks in target domain might be mapped near features of cars in source domain.
Multilingual Models for Compositional Distributed Semantics
We present a novel technique for learning semantic representations, which extends the distributional hypothesis to multilingual data and joint-space embeddings.
