Bilingual word embeddings have been widely used to capture the similarity of
lexical semantics in different human languages. However, many applications,
such as cross-lingual semantic search and question answering, can be largely
benefited from the cross-lingual correspondence between sentences and lexicons.
To bridge this gap, we propose a neural embedding model that leverages
bilingual dictionaries. The proposed model is trained to map the literal word
definitions to the cross-lingual target words, for which we explore with
different sentence encoding techniques. To enhance the learning process on
limited resources, our model adopts several critical learning strategies,
including multi-task learning on different bridges of languages, and joint
learning of the dictionary model with a bilingual word embedding model.
Experimental evaluation focuses on two applications. The results of the
cross-lingual reverse dictionary retrieval task show our model's promising
ability of comprehending bilingual concepts based on descriptions, and
highlight the effectiveness of proposed learning strategies in improving
performance. Meanwhile, our model effectively addresses the bilingual
paraphrase identification problem and significantly outperforms previous
approaches.

该研究提出了一种神经嵌入模型，利用双语词典将文字定义映射到双语目标词汇，探索不同的句子编码技术以及采用多任务学习和联合学习等关键学习策略来增强学习过程，实验结果表明，该模型在跨语言反向字典检索任务和双语释义识别问题上表现优异，并有效地解决了双语释义识别问题。