Static Fuzzy Bag-of-Words: a Lightweight and Fast Sentence Embedding Algorithm

The introduction of embedding techniques has pushed forward significantly the Natural Language Processing field. Many of the proposed solutions have been presented for word-level encoding; anyhow, in the last years, new mechanisms to treat information at a higher level of aggregation, like at sentence- and document-level, have emerged. With this work, we address specifically the sentence embeddings problem, presenting the Static Fuzzy Bag-of-Word model. Our model is a refinement of the Fuzzy Bag-of-Words approach, providing sentence embeddings with a fixed dimension. SFBoW provides competitive performances in Semantic Textual Similarity benchmarks while requiring low computational resources.