Current research on the advantages and trade-offs of using characters, instead of tokenized text, as input for deep learning models, has evolved substantially. New token-free models remove the traditional tokenization step; however, their efficiency remains unclear. Moreover, the effect of tokenization is relatively unexplored in sequence tagging tasks. To this end, we investigate the impact of tokenization when extracting information from documents and present a comparative study and analysis of subword-based and character-based models. Specifically, we study Information Extraction (IE) from biomedical texts. The main outcome is twofold: tokenization patterns can introduce inductive bias that results in state-of-the-art performance, and the character-based models produce promising results; thus, transitioning to token-free IE models is feasible.

翻译：当前关于在深度学习模型中使用字符（而非分词文本）作为输入的优劣与权衡研究已取得显著进展。新型无分词模型摒弃了传统分词步骤，但其效率仍不明确。此外，分词对序列标注任务的影响相对缺乏探究。为此，我们研究了从文档中抽取信息时分词的影响，并进行了基于子词与基于字符模型的比较分析与研究。具体而言，我们聚焦于生物医学文本的信息抽取任务。主要发现有两方面：分词模式可引入归纳偏置，从而带来最优性能；而基于字符的模型展现出令人鼓舞的结果，因此转向无分词信息抽取模型具有可行性。