论文标题
RAPO：双语词典感应的自适应排名范例
RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction
论文作者
论文摘要
双语词典感应通过用两种语言对齐独立训练的单词嵌入来诱导翻译单词。现有方法通常集中于最大程度地减少对齐对中单词之间的距离，同时却遭受了低歧视能力，以区分正面候选者和负面候选者之间的相对顺序。此外，所有单词都在全球共享映射功能，其性能可能会受到不同语言分布的偏差的阻碍。在这项工作中，我们提出了一个新颖的面向排名的归纳模型Rapo，以学习每个单词的个性化映射功能。 Rapo能够同时享受一个单词的独特特征和跨语言同构的优点。包括丰富的资源和低资源语言在内的公共数据集上的广泛实验结果证明了我们的提议的优势。我们的代码在\ url {https://github.com/jlfj345wf/rapo}中公开可用。
Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages. Existing approaches generally focus on minimizing the distances between words in the aligned pairs, while suffering from low discriminative capability to distinguish the relative orders between positive and negative candidates. In addition, the mapping function is globally shared by all words, whose performance might be hindered by the deviations in the distributions of different languages. In this work, we propose a novel ranking-oriented induction model RAPO to learn personalized mapping function for each word. RAPO is capable of enjoying the merits from the unique characteristics of a single word and the cross-language isomorphism simultaneously. Extensive experimental results on public datasets including both rich-resource and low-resource languages demonstrate the superiority of our proposal. Our code is publicly available in \url{https://github.com/Jlfj345wf/RAPO}.
