Static Fuzzy Bag-of-Words: a lightweight sentence embedding algorithm
This work addresses sentence embeddings for NLP applications, but it is incremental as it refines an existing method.
The authors tackled the problem of generating sentence embeddings by proposing the Static Fuzzy Bag-of-Words model, a refinement of an existing approach that provides competitive performance in Semantic Textual Similarity benchmarks with low computational resource requirements.
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 mechanism to treat information at an 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 predefined dimension. SFBoW provides competitive performances in Semantic Textual Similarity benchmarks, while requiring low computational resources.