We propose an approach to referring expression generation (REG) in visually grounded dialogue that is meant to produce referring expressions (REs) that are both discriminative and discourse-appropriate. Our method constitutes a two-stage process. First, we model REG as a text- and image-conditioned next-token prediction task. REs are autoregressively generated based on their preceding linguistic context and a visual representation of the referent. Second, we propose the use of discourse-aware comprehension guiding as part of a generate-and-rerank strategy through which candidate REs generated with our REG model are reranked based on their discourse-dependent discriminatory power. Results from our human evaluation indicate that our proposed two-stage approach is effective in producing discriminative REs, with higher performance in terms of text-image retrieval accuracy for reranked REs compared to those generated using greedy decoding.

本文针对视觉基础对话中的指代表达生成（REG）问题，提出了一种新方法，以生成既具区分性又适合话语的指代表达。通过两阶段的处理方法，研究发现该方法在重排名生成的指代表达方面在文本-图像检索准确性上具有显著提高效果。

视觉基础对话中的指代表达生成与话语意识理解指导