How do you handle missing data in NLP tasks?

Handling missing data in NLP tasks involves strategies to address gaps in text inputs while maintaining model performance. The approach depends on the type of missing data—whether entire sections of text are absent, specific tokens are missing, or metadata like labels is incomplete. Common methods include deletion, imputation, and model-based techniques, each with trade-offs in simplicity, data retention, and computational cost.

One straightforward method is deleting incomplete data points. For example, if a dataset contains customer reviews where some entries lack text entirely, removing those rows might be practical if the remaining data is sufficient. Similarly, in tokenized text, missing tokens (e.g., due to encoding errors) can be dropped if they’re rare. However, this risks losing valuable information, especially in small datasets. For instance, removing sentences with missing named entities in a relation extraction task could bias the model by excluding rare entity pairs. Developers should use deletion only when missingness is random and minimal, and when the dataset size allows it without harming generalization.

Imputation replaces missing values with plausible alternatives. In NLP, this might involve filling gaps with placeholders (e.g., <MASK>), statistical defaults (e.g., mean word embeddings), or predictions from auxiliary models. For example, in a sentiment analysis task with partially missing reviews, a language model like BERT could predict missing words based on context. For missing labels—common in weakly supervised learning—heuristic rules or crowd-sourced annotations might fill gaps. A practical example is using Word2Vec’s average vector for a sentence when a word is missing, though this can dilute semantic nuances. Imputation works best when the missing data has predictable patterns, but it requires careful validation to avoid introducing noise.

Model-based approaches handle missing data inherently through architecture design. Techniques like attention mechanisms or dropout can make models robust to missing inputs. For instance, transformer models ignore padding tokens dynamically during self-attention computations. In sequence tagging tasks, bidirectional LSTMs can infer missing context by leveraging surrounding words. Another approach is training on artificially corrupted data (e.g., randomly masking tokens) to teach the model to handle gaps—a method used in BERT’s pretraining. For missing labels, techniques like semi-supervised learning (e.g., self-training with pseudo-labels) or multi-task learning (sharing representations across related tasks) can compensate. These methods often require more computational resources but minimize information loss compared to deletion or imputation.