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Prior studies diagnose the anisotropy problem in sentence representationsfrom pre-trained language models, e.g., BERT, without fine-tuning. Our analysisreveals that the sentence embeddings from BERT suffer from a bias towardsuninformative words, limiting the performance in semantic textual similarity(STS) tasks. To address this bias, we propose a simple and efficientunsupervised approach, Diagonal Attention Pooling (Ditto), which weights wordswith model-based importance estimations and computes the weighted average ofword representations from pre-trained models as sentence embeddings. Ditto canbe easily applied to any pre-trained language model as a postprocessingoperation. Compared to prior sentence embedding approaches, Ditto does not addparameters nor requires any learning. Empirical evaluations demonstrate thatour proposed Ditto can alleviate the anisotropy problem and improve variouspre-trained models on STS tasks.

Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings