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Found 8 papers, 6 papers with code
The Impact of Syntactic and Semantic Proximity on Machine Translation with Back-Translation
Unsupervised on-the-fly back-translation, in conjunction with multilingual pretraining, is the dominant method for unsupervised neural machine translation.
Metric-Learning Encoding Models Identify Processing Profiles of Linguistic Features in BERT's Representations
Together, this demonstrates the utility of Metric-Learning Encoding Methods for studying how linguistic features are neurally encoded in language models and the advantage of MLEMs over traditional methods.
Bridging the Empirical-Theoretical Gap in Neural Network Formal Language Learning Using Minimum Description Length
Neural networks offer good approximation to many tasks but consistently fail to reach perfect generalization, even when theoretical work shows that such perfect solutions can be expressed by certain architectures.
Minimum Description Length Hopfield Networks
Associative memory architectures are designed for memorization but also offer, through their retrieval method, a form of generalization to unseen inputs: stored memories can be seen as prototypes from this point of view.
Benchmarking Neural Network Generalization for Grammar Induction
How well do neural networks generalize?
Minimum Description Length Recurrent Neural Networks
We train neural networks to optimize a Minimum Description Length score, i. e., to balance between the complexity of the network and its accuracy at a task.
On the Spontaneous Emergence of Discrete and Compositional Signals
We propose a general framework to study language emergence through signaling games with neural agents.
