431 citations · 857 across the 69 of their papers we have counts for
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stat.ML2017★ 431 cited
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
Bjarke Felbo, Alan Mislove, Anders Søgaard +2
NLP tasks are often limited by scarcity of manually annotated data. In social media sentiment analysis and related tasks, researchers have therefore used binarized emoticons and sp…
stat.ML2017
Latent Multi-task Architecture Learning
Sebastian Ruder, Joachim Bingel, Isabelle Augenstein +1
Multi-task learning (MTL) allows deep neural networks to learn from related tasks by sharing parameters with other networks. In practice, however, MTL involves searching an enormou…