  Classification algorithms using Transformer architectures can be affected by
the sequence length learning problem whenever observations from different
classes have a different length distribution. This problem causes models to use
sequence length as a predictive feature instead of relying on important textual
information. Although most public datasets are not affected by this problem,
privately owned corpora for fields such as medicine and insurance may carry
this data bias. The exploitation of this sequence length feature poses
challenges throughout the value chain as these machine learning models can be
used in critical applications. In this paper, we empirically expose this
problem and present approaches to minimize its impacts.
