Proceedings of the Annual Meeting of the Cognitive Science Society

Ontology is the basis of knowledge representation, and it is necessary to translate ontologies that are normally expressed in English into other languages in order to achieve exchange across languages. Building a domain-specific translation system is essential due to the extremely focused words used and the inadequacy of contextual information. In this paper, we introduce disentangled representations under cross-lingual agreement to alleviate the aforementioned issues. We introduce semantic and language representations and integrate extra losses to induce disentangled representations that capture different information. To reduce the gap between the ontology label and the hypothesis generated by the translation model, we further integrate adversarial learning. In order to guide the generation of translation candidates, the semantic matching strategy is incorporated into the decoding phase. Experiments on the four English-to-German ontologies of different domains show that the proposed method achieves improvements over the baselines.