Cross-modal retrieval is essential for interpreting cultural heritage data, but its effectiveness is often limited by incomplete or inconsistent textual descriptions, caused by historical data loss and the high cost of expert annotation.While large language models (LLMs) offer a promising solution by enriching textual descriptions, their outputs frequently suffer from hallucinations or miss visually grounded details.To address these challenges, we propose C 3 , a data augmentation framework that enhances cross-modal retrieval performance by improving the completeness and consistency of LLM-generated descriptions.C 3 introduces a completeness evaluation module to assess semantic coverage using both visual cues and language-model outputs.Furthermore, to mitigate factual inconsistencies, we formulate a Markov Decision Process to supervise Chain-of-Thought reasoning, guiding consistency evaluation through adaptive query control.Experiments on the cultural heritage datasets CulTi and TimeTravel, as well as on general benchmarks MSCOCO and Flickr30K, demonstrate that C 3 achieves state-of-the-art performance in both fine-tuned and zero-shot settings.The code of this paper is available at https://github.com/JianZhang24/C-3.

The task-oriented dialogue systems aim to assist the users in completing specific tasks through natural language dialogue. Recently, word-level dialogue state tracking (DST) has become a core component of task-oriented dialogue systems. In this paper, we study the word-level DST task at the 8th dialogue system technology challenge (DSTC8), namely schema-guided dialogue state tracking, which focuses on cross-domain dialogue state tracking and zero-shot generalization to new services. Many approaches have been proposed to exploit the schema description for dialogue modeling, especially on unseen services. Despite their success, existing methods still suffer from two weaknesses: (1) the current methods do not fully exploit the dialogue history, which makes it difficult to solve the slot carryover problem from the multi-domain dialogues; (2) the current method treats the task as four independent sub tasks without considering the relevance of the subtasks. To address these issues, we propose a novel two-stage framework for schema-guided dialogue state tracking with selected dialogue history (TS-DST). Specifically, to solve the first issue, we propose a novel utterance selection module to select the most related previous utterances from the dialogue history by considering the specific schema element. To solve the second issue, we propose a two-stage framework to solve the four subtasks. Experiments conducted on the SGD dataset show that our method achieves new state-of-the-art performance. We also conduct ablation studies to demonstrate the effectiveness of the utterance selection module and the two-stage strategy.