  The rapid development of large pre-trained language models has greatly
increased the demand for model compression techniques, among which quantization
is a popular solution. In this paper, we propose BinaryBERT, which pushes BERT
quantization to the limit with weight binarization. We find that a binary BERT
is hard to be trained directly than a ternary counterpart due to its complex
and irregular loss landscapes. Therefore, we propose ternary weight splitting,
which initializes the binary model by equivalent splitting from a half-sized
ternary network. The binary model thus inherits the good performance of the
ternary model, and can be further enhanced by fine-tuning the new architecture
after splitting. Empirical results show that BinaryBERT has negligible
performance drop compared to the full-precision BERT-base while being
$24\times$ smaller, achieving the state-of-the-art results on GLUE and SQuAD
benchmarks.
