On the one hand, we introduce a family of losses that are robust to non-identical class distributions, enabling clients to train a generic predictor with a consistent objective across them.
Chinese NLP applications that rely on large text often contain huge amounts of vocabulary which are sparse in corpus.
We investigate learning a ConvNet classifier with class-imbalanced data.
The objective of non-parallel text style transfer, or controllable text generation, is to alter specific attributes (e. g. sentiment, mood, tense, politeness, etc) of a given text while preserving its remaining attributes and content.
State-of-the-art deep neural networks (DNNs) typically have tens of millions of parameters, which might not fit into the upper levels of the memory hierarchy, thus increasing the inference time and energy consumption significantly, and prohibiting their use on edge devices such as mobile phones.
Lexicon relation extraction given distributional representation of words is an important topic in NLP.