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Don't Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors
[article]
2019
arXiv
pre-print
Recent literature suggests that averaged word vectors followed by simple post-processing outperform many deep learning methods on semantic textual similarity tasks. Furthermore, when averaged word vectors are trained supervised on large corpora of paraphrases, they achieve state-of-the-art results on standard STS benchmarks. Inspired by these insights, we push the limits of word embeddings even further. We propose a novel fuzzy bag-of-words (FBoW) representation for text that contains all the
arXiv:1904.13264v1
fatcat:msehfx2psng4zgu66q5utxc5sa