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Found 8 papers, 4 papers with code
Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations
Predominantly, two formulations are used for sentence-pair tasks: bi-encoders and cross-encoders.
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Cooperative Learning of Disjoint Syntax and Semantics
There has been considerable attention devoted to models that learn to jointly infer an expression's syntactic structure and its semantics.
Embedding Words as Distributions with a Bayesian Skip-gram Model
Rather than assuming that a word embedding is fixed across the entire text collection, as in standard word embedding methods, in our Bayesian model we generate it from a word-specific prior density for each occurrence of a given word.
Obfuscation for Privacy-preserving Syntactic Parsing
We introduce an idea for a privacy-preserving transformation on natural language data, inspired by homomorphic encryption.
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols
Learning to communicate through interaction, rather than relying on explicit supervision, is often considered a prerequisite for developing a general AI.
The Emergence of Compositional Languages for Numeric Concepts Through Iterated Learning in Neural Agents
Since first introduced, computer simulation has been an increasingly important tool in evolutionary linguistics.
Preventing Posterior Collapse with Levenshtein Variational Autoencoder
Variational autoencoders (VAEs) are a standard framework for inducing latent variable models that have been shown effective in learning text representations as well as in text generation.
Just Mix Once: Worst-group Generalization by Group Interpolation
Advances in deep learning theory have revealed how average generalization relies on superficial patterns in data.
