Learning and Evaluating Sparse Interpretable Sentence Embeddings
This work addresses the need for interpretable sentence embeddings for NLP researchers and practitioners, though it is incremental as it transfers ideas from word embeddings to sentences.
The paper tackled the problem of creating interpretable sentence embeddings by exploring sparse representations, and introduced a novel automated evaluation metric based on topic coherence, resulting in increased interpretability on movie dialogs and MS COCO scene descriptions.
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.