China has over 1.116 billion mobile Internet users, whose demands continue to evolve rapidly. However, existing Chinese spoken language understanding (SLU) benchmarks are outdated and cover limited domains, failing to reflect real-world mobile assistant scenarios. To this end, we introduce CMPA-SLU, a Chinese SLU dataset for mobile personal assistants collected from real-world applications and annotated with rigorous quality control. CMPA-SLU contains 50 domains, 247 intents, and 74 slot types, expanding the diversity of existing Chinese SLU benchmarks. To better simulate practical deployment scenarios, we further introduce a Partial Category setting, where candidate labels are dynamically filtered using a Bidirectional Guided Embedding (BGE)-based retrieval strategy. We further adopt a label mapping strategy for joint intent and slot prediction within a single generation process. We evaluate multiple large language models (LLMs) under both Full Category and Partial Category settings. Experimental results show that the Partial Category setting improves performance by reducing the candidate label space.

With the rapid advancement of large language models (LLMs), recent researchers have increasingly focused on the superior capabilities of LLMs in text/code understanding and generation to tackle text-to-SQL tasks. Traditional approaches adopt schema linking to first eliminate redundant tables and columns and prompt LLMs for SQL generation. However, they often struggle with accurately identifying corresponding tables and columns, due to discrepancies in naming conventions between natural language questions (NL) and database schemas. Besides, existing methods overlook the challenge of effectively transforming structure information from NL into SQL. To address these limitations, we introduce UCS-SQL, a novel text-to-SQL framework, uniting both content and structure pipes to bridge the gap between NL and SQL. Specifically, the content pipe focuses on identifying key content within the original content, while the structure pipe is dedicated to transforming the linguistic structure from NL to SQL. Additionally, we strategically selects few-shot examples by considering both the SQL Skeleton and Question Expression (SS-QE selection method), thus providing targeted examples for SQL generation. Experimental results on BIRD and Spider demonstrate the effectiveness of our UCS-SQL framework.

We leverage cross-language data expansion and retraining to enhance neural Event Detection (abbr., ED) on English ACE corpus. Machine translation is utilized for expanding English training set of ED from that of Chinese. However, experimental results illustrate that such strategy actually results in performance degradation. The survey of translations suggests that the mistakenly-aligned triggers in the expanded data negatively influences the retraining process. We refer this phenomenon to “trigger falsification”. To overcome the issue, we apply heuristic rules for regulating the expanded data, fixing the distracting samples that contain the falsified triggers. The supplementary experiments show that the rule-based regulation is beneficial, yielding the improvement of about 1.6% F1-score for ED. We additionally prove that, instead of transfer learning from the translated ED data, the straight data combination by random pouring surprisingly performs better.