Pre-trained language models like BERT have achieved great success in a wide
variety of NLP tasks, while the superior performance comes with high demand in
computational resources, which hinders the application in low-latency IR
systems. We present TwinBERT model for effective and efficient retrieval, which
has twin-structured BERT-like encoders to represent query and document
respectively and a crossing layer to combine the embeddings and produce a
similarity score. Different from BERT, where the two input sentences are
concatenated and encoded together, TwinBERT decouples them during encoding and
produces the embeddings for query and document independently, which allows
document embeddings to be pre-computed offline and cached in memory. Thereupon,
the computation left for run-time is from the query encoding and query-document
crossing only. This single change can save large amount of computation time and
resources, and therefore significantly improve serving efficiency. Moreover, a
few well-designed network layers and training strategies are proposed to
further reduce computational cost while at the same time keep the performance
as remarkable as BERT model. Lastly, we develop two versions of TwinBERT for
retrieval and relevance tasks correspondingly, and both of them achieve close
or on-par performance to BERT-Base model.
The model was trained following the teacher-student framework and evaluated
with data from one of the major search engines. Experimental results showed
that the inference time was significantly reduced and was firstly controlled
around 20ms on CPUs while at the same time the performance gain from fine-tuned
BERT-Base model was mostly retained. Integration of the models into production
systems also demonstrated remarkable improvements on relevance metrics with
negligible influence on latency.

TwinBERT 模型可用于低延时 IR 系统中进行有效且高效的召回，通过将查询和文档的嵌入交叉组合来生成相似性分数，并且可以预先计算文档嵌入并将其缓存在内存中，从而显着提高了处理效率。