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Found 7 papers, 4 papers with code
Towards more patient friendly clinical notes through language models and ontologies
Our method based on a language model trained on medical forum data generates simpler sentences while preserving both grammar and the original meaning, surpassing the current state of the art.
Estimating Mutual Information Between Dense Word Embeddings
In this work we go through a vast literature on estimating MI in such cases and single out the most promising methods, yielding a simple and elegant similarity measure for word embeddings.
Correlations between Word Vector Sets
Just like cosine similarity is used to compare individual word vectors, we introduce a novel application of the centered kernel alignment (CKA) as a natural generalisation of squared cosine similarity for sets of word vectors.
Neural Language Priors
The choice of sentence encoder architecture reflects assumptions about how a sentence's meaning is composed from its constituent words.
Correlation Coefficients and Semantic Textual Similarity
Importantly, we show that Pearson correlation is appropriate for some word vectors but not others.
Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors
Recent literature suggests that averaged word vectors followed by simple post-processing outperform many deep learning methods on semantic textual similarity tasks.
Decoding Decoders: Finding Optimal Representation Spaces for Unsupervised Similarity Tasks
Experimental evidence indicates that simple models outperform complex deep networks on many unsupervised similarity tasks.
