发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对CLEF 2026 JOKER任务2，提出基于图的可供性检索与多评估者排名的双关语翻译方法，证实检索音义可供性是计算双关语翻译的核心瓶颈，结果契合Low的设想。
AI 中文摘要
15年前，Low提出双关语译者不应再寻找对等词汇，而应探索音与义之间的新接触点。本文从计算角度研究这一想法，将双关语翻译建模为发现、探索与选择的过程：检索系统在语义和音系邻域中搜索目标语言可供性（支持新文字游戏的音义桥梁）；多个语言模型通过生成竞争译本来探索这些机会；多视角生成-排名架构从中进行选择。除系统开发外，本文的主要贡献是分析检索到的可供性如何在翻译过程中传播：研究发现生成器会主动利用检索到的机会，评估者会逐步集中于更强的音义桥梁，若存在精确音系碰撞则会以不成比例的高比例被选中；同时，许多双关语仍无法产生可用的可供性，表明检索仍是计算双关语翻译的核心瓶颈。最终结果与Low设想的过程高度契合，成功的双关语翻译并非源于保留源语言词汇，而是源于发现目标语言中音与义碰撞的新位置。
英文摘要
Fifteen years ago, Low proposed that pun translators should stop searching for equivalent words and instead search for new points of contact between sound and meaning. In this paper, we investigate that idea computationally. We model pun translation as a process of discovery, exploration, and selection. A retrieval system searches semantic and phonological neighborhoods for target-language affordances: sound-meaning bridges that may support new wordplay. Multiple language models then explore these opportunities by generating competing translations, while a multi-perspective generate-and-rank architecture selects among them. Beyond system development, our primary contribution is an analysis of how retrieved affordances propagate through the translation process. We find that generators actively exploit retrieved opportunities, evaluators progressively concentrate around stronger sound-meaning bridges, and exact phonological collisions are selected at disproportionately high rates when available. At the same time, many puns still yield no usable affordances, suggesting that retrieval remains the central bottleneck in computational pun translation. The resulting picture is remarkably close to the process envisioned by Low. Successful pun translation emerges not from preserving source-language words, but from discovering new places in the target language where sound and meaning collide.
CommentsCLEF 2026 Working Notes, 21-24 September 2026, Jena, Germany
