Unsupervised Morphological Tree Tokenizer
This addresses tokenization issues in natural language processing for researchers and practitioners, though it is incremental as it builds on existing tokenization methods with morphological enhancements.
The paper tackled the problem of tokenization disrupting word boundaries by introducing a deep model that uses morphological structure guidance to induce character-level structures without annotated data, resulting in outperforming BPE and WordPiece on morphological segmentation and language modeling tasks.
As a cornerstone in language modeling, tokenization involves segmenting text inputs into pre-defined atomic units. Conventional statistical tokenizers often disrupt constituent boundaries within words, thereby corrupting semantic information. To address this drawback, we introduce morphological structure guidance to tokenization and propose a deep model to induce character-level structures of words. Specifically, the deep model jointly encodes internal structures and representations of words with a mechanism named $\textit{MorphOverriding}$ to ensure the indecomposability of morphemes. By training the model with self-supervised objectives, our method is capable of inducing character-level structures that align with morphological rules without annotated training data. Based on the induced structures, our algorithm tokenizes words through vocabulary matching in a top-down manner. Empirical results indicate that the proposed method effectively retains complete morphemes and outperforms widely adopted methods such as BPE and WordPiece on both morphological segmentation tasks and language modeling tasks. Code is available at https://github.com/martianmartina/TreeTokenizer.