# Learning and Evaluating Sparse Interpretable Sentence Embeddings Source: [Learning and Evaluating Sparse Interpretable Sentence Embeddings]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:55:49.570Z Authors: Valentin Trifonov, Octavian-Eugen Ganea, Anna Potapenko, Thomas Hofmann Published: 2018-09-23T16:02:03.000Z ## Abstract Previous research on word embeddings has shown that sparse representations, which can be either learned on top of existing dense embeddings or obtained through model constraints during training time, have the benefit of increased interpretability properties: to some degree, each dimension can be understood by a human and associated with a recognizable feature in the data. In this paper, we transfer this idea to sentence embeddings and explore several approaches to obtain a sparse representation. We further introduce a novel, quantitative and automated evaluation metric for sentence embedding interpretability, based on topic coherence methods. We observe an increase in interpretability compared to dense models, on a dataset of movie dialogs and on the scene descriptions from the MS COCO dataset. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()