We present an information retrieval based reverse dictionary system using modern pre-trained language models and approximate nearest neighbors search algorithms. The proposed approach is applied to an existing Estonian language lexicon resource, S\~onaveeb (word web), with the purpose of enhancing and enriching it by introducing cross-lingual reverse dictionary functionality powered by semantic search. The performance of the system is evaluated using both an existing labeled English dataset of words and definitions that is extended to contain also Estonian and Russian translations, and a novel unlabeled evaluation approach that extracts the evaluation data from the lexicon resource itself using synonymy relations. Evaluation results indicate that the information retrieval based semantic search approach without any model training is feasible, producing median rank of 1 in the monolingual setting and median rank of 2 in the cross-lingual setting using the unlabeled evaluation approach, with models trained for cross-lingual retrieval and including Estonian in their training data showing superior performance in our particular task.

翻译：我们提出了一种基于信息检索的反向词典系统，该系统采用现代预训练语言模型和近似最近邻搜索算法。所提出的方法应用于现有的爱沙尼亚语词汇资源Sõnaveeb（词网），通过引入由语义搜索驱动的跨语言反向词典功能，旨在增强并丰富该资源。系统性能通过两种方式评估：一是使用已有的标注英文单词-定义数据集（扩展后包含爱沙尼亚语和俄语译文），二是采用一种新颖的无标注评估方法，通过同义关系从词汇资源本身提取评估数据。评估结果表明，基于信息检索的语义搜索方法无需任何模型训练即可实现可行效果：在无标注评估中，单语言设置的中位数排名为1，跨语言设置的中位数排名为2；而在跨语言检索训练数据中包含爱沙尼亚语的模型，在我们特定任务中表现出更优性能。