Social media data has been of interest to Natural Language Processing (NLP) practitioners for over a decade, because of its richness in information, but also challenges for automatic processing. Since language use is more informal, spontaneous, and adheres to many different sociolects, the performance of NLP models often deteriorates. One solution to this problem is to transform data to a standard variant before processing it, which is also called lexical normalization. There has been a wide variety of benchmarks and models proposed for this task. The MultiLexNorm benchmark proposed to unify these efforts, but it consists almost solely of languages from the Indo-European language family in the Latin script. Hence, we propose an extension to MultiLexNorm, which covers five Asian languages from different language families in four different scripts. We show that the previous state-of-the-art model performs worse on the new languages and propose a new architecture based on Large Language Models (LLMs), which shows more robust performance. Finally, we analyze remaining errors, revealing future directions for this task.

Text-to-SQL aims to convert natural language questions into executable SQL queries.While previous approaches, such as skeleton-masked selection, have demonstrated strong performance by retrieving similar training examples to guide large language models (LLMs), they struggle in real-world scenarios where such examples are unavailable.To overcome this limitation, we propose Self-Augmentation incontext learning with Fine-grained Example selection for Text-to-SQL (SAFE-SQL), a novel unsupervised framework that enhances SQL generation by generating and intelligently filtering self-augmented examples.SAFE-SQL leverages an LLM to generate diverse Textto-SQL examples, which are then filtered by a novel fine-grained mechanism using criteria for semantic similarity, structural alignment, and reasoning path quality to curate highquality in-context learning examples.Leveraging these carefully selected self-generated examples, SAFE-SQL significantly surpasses previous zero-shot and few-shot Text-to-SQL frameworks, achieving superior execution accuracy.Notably, our approach demonstrates substantial performance gains in challenging extra hard and unseen scenarios, where conventional methods often struggle.