The quality of video subtitle translation cannot be assessed from sentence-level semantic equivalence alone. Subtitles are governed by audiovisual cues, speaker relationships, and narrative context that accumulates across scenes, yet existing automatic metrics and text-centric LLM-based evaluation do not reflect these factors.

We propose SCR-Eval, a context-aware evaluation framework for video subtitle translation, built on a Script-like Context Representation (SCR) reconstructed from the video. The evaluator aligns the translated subtitles to the SCR’s semantic scenes, then performs MQM error analysis with an LLM judge.

On three open movies, we benchmark five English-to-Korean subtitle providers against blind, randomized annotations by seven professional evaluators. The framework’s provider ranking agrees strongly with the experts (system-level Spearman ρ = 0.967). In ablations, agreement weakens stepwise from full context through scene-bounded text to isolated source–translation pairs. Automatic baselines agree less: MetricX and xCOMET correlate negatively with the expert ranking, while RUBRIC-MQM correlates only weakly.

With full context, SCR-Eval shows substantially stronger agreement with experts than these baselines.

Recent advancements in large language models (LLMs) have boosted research on generating SQL queries from domain-specific questions, particularly in the medical domain. A key challenge is detecting and filtering unanswerable questions. Existing methods often relying on model uncertainty, but these require extra resources and lack interpretability. We propose a lightweight model that predicts relevant database schemas to detect unanswerable questions, enhancing interpretability and addressing the data imbalance in binary classification tasks. Furthermore, we found that LLM-generated schema descriptions can significantly enhance the prediction accuracy. Our method provides a resource-efficient solution for unanswerable question detection in domain-specific question answering systems.

Recently, deep learning-based language models have significantly enhanced text-to-SQL tasks, with promising applications in retrieving patient records within the medical domain. One notable challenge in such applications is discerning unanswerable queries. Through fine-tuning model, we demonstrate the feasibility of converting medical record inquiries into SQL queries. Additionally, we introduce an entropy-based method to identify and filter out unanswerable results. We further enhance result quality by filtering low-confidence SQL through log probability-based distribution, while grammatical and schema errors are mitigated by executing queries on the actual database.We experimentally verified that our method can filter unanswerable questions, which can be widely utilized even when the parameters of the model are not accessible, and that it can be effectively utilized in practice.