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Found 9 papers, 4 papers with code
Controlling Conditional Language Models without Catastrophic Forgetting
Machine learning is shifting towards general-purpose pretrained generative models, trained in a self-supervised manner on large amounts of data, which can then be applied to solve a large number of tasks.
The emergence of number and syntax units in LSTM language models
Importantly, the behaviour of these units is partially controlled by other units independently shown to track syntactic structure.
What you can cram into a single \$\&!\#* vector: Probing sentence embeddings for linguistic properties
Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing.
Learning compositionally through attentive guidance
While neural network models have been successfully applied to domains that require substantial generalisation skills, recent studies have implied that they struggle when solving the task they are trained on requires inferring its underlying compositional structure.
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Although much effort has recently been devoted to training high-quality sentence embeddings, we still have a poor understanding of what they are capturing.
Deriving Boolean structures from distributional vectors
Corpus-based distributional semantic models capture degrees of semantic relatedness among the words of very large vocabularies, but have problems with logical phenomena such as entailment, that are instead elegantly handled by model-theoretic approaches, which, in turn, do not scale up.
