PUNCTMASK

PUNCTMASK is a technique in natural language processing designed to mask punctuation in text data as part of preprocessing, training, and evaluation of language models. The approach replaces punctuation characters with a dedicated mask token or with a punct-type tag, allowing researchers to study how models encode information that would otherwise be signaled by punctuation and to reduce reliance on punctuation cues in noisy or multilingual text.

PUNCTMASK can be applied in several forms. In token-level masking, each punctuation character is replaced with a general mask (for example <PUNCT>). In type-level masking, entire classes of punctuation, such as commas or periods, are masked uniformly (for example <PUNCT_COMMA>, <PUNCT_PERIOD>). Masking probability is configurable and can be static or dynamic during training. The technique is often used during pretraining of masked language models, as a data augmentation strategy, or in controlled experiments to assess the impact of punctuation on task performance.

Practical considerations include language coverage, as punctuation inventories differ across languages, and tokenization schemes, since subword models must preserve alignment between masks and targets. When applied, PUNCTMASK can be combined with other masking strategies and carefully evaluated to avoid introducing bias or harming downstream tasks such as parsing or sentiment analysis.

Example: "Hello, world!" with a comma and exclamation have masked punctuation could become "Hello <PUNCT_COMMA> world <PUNCT_EXCL>." See also punctuation handling, masked language modeling, and data augmentation in NLP.