Translating Terminological Expressions in Knowledge Bases with Neural Machine Translation
This work addresses the challenge of translating specialized terminology in knowledge bases for domains like medicine and finance, but it is incremental as it builds on existing machine translation methods.
The paper tackled the problem of translating domain-specific terminological expressions in knowledge bases, which suffer from ambiguous vocabulary and lack of context, by evaluating statistical and neural machine translation methods with domain adaptation and external terminology injection. The results showed significant advantages in domain adaptation for medical and financial domains and that subword models outperformed word-based neural models for terminology translation.
Our work presented in this paper focuses on the translation of terminological expressions represented in semantically structured resources, like ontologies or knowledge graphs. The challenge of translating ontology labels or terminological expressions documented in knowledge bases lies in the highly specific vocabulary and the lack of contextual information, which can guide a machine translation system to translate ambiguous words into the targeted domain. Due to these challenges, we evaluate the translation quality of domain-specific expressions in the medical and financial domain with statistical as well as with neural machine translation methods and experiment domain adaptation of the translation models with terminological expressions only. Furthermore, we perform experiments on the injection of external terminological expressions into the translation systems. Through these experiments, we observed a significant advantage in domain adaptation for the domain-specific resource in the medical and financial domain and the benefit of subword models over word-based neural machine translation models for terminology translation.