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Related Questions
- How do techniques like synonym substitution, back-translation, and paraphrasing help address data sparsity?
- What are some examples of using inverse indexing or graph-based methods to supplement language models with unseen data?
- How does entity masking or contextual substitution modify the original input text and improve the language model's understanding of underrepresented terms?
- What's the effect of random mask insertion on increasing diversity of training data, including usage of infrequent and polysemous words?
- What do authors and researchers recommend using multiple masked language modeling methods concurrently as a combined solution for boosting low-frequency vocabulary performance, and can their work and results be further enhanced via the proposed integration method presented in a hypothetical experiment
- Would submodular pick of unvisited entities as new target subsets for inferring knowledge around low-frequenty used words effectively promote and contribute to increased domain vocabulary depth and contextual interpretation ability among language models during test mode?
- Could some form of generative techniques, specifically combining reinforcement with adversarial noise generation schemes, augment datasets further on the fly so the vocabulary of less-representative words might still endow the deep learner, by way of their induced capacity to identify semantically less common representations via these two methods acting independently with less noise when tested.
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