  In this work, we investigate the positional encoding methods used in language
pre-training (e.g., BERT) and identify several problems in the existing
formulations. First, we show that in the absolute positional encoding, the
addition operation applied on positional embeddings and word embeddings brings
mixed correlations between the two heterogeneous information resources. It may
bring unnecessary randomness in the attention and further limit the
expressiveness of the model. Second, we question whether treating the position
of the symbol \texttt{[CLS]} the same as other words is a reasonable design,
considering its special role (the representation of the entire sentence) in the
downstream tasks. Motivated from above analysis, we propose a new positional
encoding method called \textbf{T}ransformer with \textbf{U}ntied
\textbf{P}ositional \textbf{E}ncoding (TUPE). In the self-attention module,
TUPE computes the word contextual correlation and positional correlation
separately with different parameterizations and then adds them together. This
design removes the mixed and noisy correlations over heterogeneous embeddings
and offers more expressiveness by using different projection matrices.
Furthermore, TUPE unties the \texttt{[CLS]} symbol from other positions, making
it easier to capture information from all positions. Extensive experiments and
ablation studies on GLUE benchmark demonstrate the effectiveness of the
proposed method. Codes and models are released at
https://github.com/guolinke/TUPE.

Computation and Language;Machine Learning