  This paper presents a novel pre-trained language models (PLM) compression
approach based on the matrix product operator (short as MPO) from quantum
many-body physics. It can decompose an original matrix into central tensors
(containing the core information) and auxiliary tensors (with only a small
proportion of parameters). With the decomposed MPO structure, we propose a
novel fine-tuning strategy by only updating the parameters from the auxiliary
tensors, and design an optimization algorithm for MPO-based approximation over
stacked network architectures. Our approach can be applied to the original or
the compressed PLMs in a general way, which derives a lighter network and
significantly reduces the parameters to be fine-tuned. Extensive experiments
have demonstrated the effectiveness of the proposed approach in model
compression, especially the reduction in finetuning parameters (91% reduction
on average).
