Reducing Sequence Length Learning Impacts on Transformer Models
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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 brings models to use sequence length as a predictive feature instead of relying on important textual information. Even if most public datasets are not affected by this problem, privately corpora for fields such as medicine and insurance may carry this data bias. This poses challenges throughout the value chain given their usage in a machine learning application. In this paper, we empirically expose this problem and present approaches to minimize its impacts.
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