Papers › An Embarrassingly Simple Method to Mitigate Undesirable Properties of Pretrained...
An Embarrassingly Simple Method to Mitigate Undesirable Properties of Pretrained Language Model Tokenizers
Valentin Hofmann, Hinrich Schuetze, Janet Pierrehumbert
We introduce FLOTA (Few Longest Token Approximation), a simple yet effective method to improve the tokenization of pretrained language models (PLMs). FLOTA uses the vocabulary of a standard tokenizer but tries to preserve the morphological structure of words during tokenization. We evaluate FLOTA on morphological gold segmentations as well as a text classification task, using BERT, GPT-2, and XLNet as example PLMs. FLOTA leads to performance gains, makes inference more efficient, and enhances the robustness of PLMs with respect to whitespace noise.
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