Language Sampling

Language sampling focuses on selecting subsets of linguistic data for efficient and effective natural language processing (NLP) tasks. Current research emphasizes developing principled methods for creating diverse and representative language samples, often leveraging linguistic typology and incorporating techniques from Markov Chain Monte Carlo (MCMC) and constrained optimization to address challenges like imbalanced datasets and the need for efficient training. These improved sampling strategies aim to enhance the generalizability and fairness of multilingual NLP models, leading to more robust and equitable performance across diverse languages and applications. The impact extends to improving the efficiency of model training and mitigating biases stemming from skewed data distributions.