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Mitigating Contradictions in Dialogue Based on Contrastive Learning
Chatbot models have achieved remarkable progress in recent years but tend to yield contradictory responses.
Learning Large-scale Network Embedding from Representative Subgraph
Then, NES computes the network embedding from this representative subgraph, efficiently.
Fast Extraction of Word Embedding from Q-contexts
In this work, we show that with merely a small fraction of contexts (Q-contexts)which are typical in the whole corpus (and their mutual information with words), one can construct high-quality word embedding with negligible errors.
TSDG: Content-aware Neural Response Generation with Two-stage Decoding Process
Neural response generative models have achieved remarkable progress in recent years but tend to yield irrelevant and uninformative responses.
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