How to learn highly compact yet effective sentence representation?
Pre-trained language models have been effective in many NLP tasks. However,
these models are often huge and produce large sentence embeddings. Moreover,
there is a big performance gap between large and small models. In this paper,
we propose Homomorphic Projective Distillation (HPD) to learn compressed
sentence embeddings. Our method augments a small Transformer encoder model with
learnable projection layers to produce compact representations while mimicking
a large pre-trained language model to retain the sentence representation
quality. We evaluate our method with different model sizes on both semantic
textual similarity (STS) and semantic retrieval (SR) tasks. Experiments show
that our method achieves 2.7-4.5 points performance gain on STS tasks compared
with previous best representations of the same size. In SR tasks, our method
improves retrieval speed (8.2$\times$) and memory usage (8.0$\times$) compared
with state-of-the-art large models.

这篇研究提出了 Homomorphic Projective Distillation（HPD）方法来学习压缩的句子嵌入，通过增加可学习的投影层来模拟大型预训练语言模型并保留句子表示质量。作者通过不同大小的模型对语义文本相似性（STS）和语义检索（SR）任务进行了评估，实验结果表明，相比于相同大小的最佳表示方法，该方法在 STS 任务上实现了 2.7-4.5 个性能提升，在 SR 任务上，该方法相比于最先进的大型模型，提高了检索速度（8.2 倍）和内存使用（8.0 倍）。