Existing user simulation approaches focus on generating user-like responses in dialogue. They often assume that the provided persona is sufficient for producing such responses, without verifying whether critical personas are supplied. This raises concerns about the validity of simulation results.To address this issue, we study the task of identifying persona dimensions (e.g., ”whether the user is price-sensitive”) that are relevant but missing in simulating a user’s reply for a given dialogue context.We introduce PICQ-drama (constructed from TVShowGuess), a benchmark of context-aware choice questions, annotated with missing persona dimensions whose absence leads to ambiguous user choices. We further design diverse evaluation criteria for missing persona identification.Benchmarking leading LLMs on our PICQ-drama dataset demonstrates the feasibility of this task. Evaluation across diverse criteria, along with further analyses, reveals cognitive differences between LLMs and humans and highlights the distinct roles of different persona categories in shaping responses.

Subtitles play a crucial role in improving the accessibility of the vast amount of audiovisual content available on the Internet, allowing audiences worldwide to comprehend and engage with this content in various languages. Automatic subtitling (AS) systems are essential for alleviating the substantial workload of human transcribers and translators. However, existing AS corpora and the primary metric SubER focus on European languages. This paper introduces A-TASC, an Asian TED-based automatic subtitling corpus derived from English TED Talks, comprising nearly 800 hours of audio segments, aligned English transcripts, and subtitles in Chinese, Japanese, Korean, and Vietnamese. We then present SacreSubER, a modification of SubER, to enable the reliable evaluation of subtitle quality for languages without explicit word boundaries. Experimental results, using both end-to-end systems and pipeline approaches built on strong ASR and LLM components, validate the quality of the proposed corpus and reveal differences in AS performance between European and Asian languages. The code to build our corpus is released.