Cross-lingual Neural Vector Conceptualization

Lisa Raithel, Robert Schwarzenberg

In: NLPCC 2019 Workshop on Explainable Artificial Intelligence. NLPCC Workshop on Explainable Artificial Intelligence (XAI-2019) befindet sich 8th CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC2019) October 12-12 Dunhuang China Lecture Notes in Artificial Intelligence (LNA) Springer 2019.


Recently, Neural Vector Conceptualization (NVC) was proposed as a means to interpret samples from a word vector space. For NVC, a neural model activates higher order concepts it recognizes in a word vector instance. To this end, the model first needs to be trained with a sufficiently large instance-to-concept ground truth, which only exists for a few languages. In this work, we tackle this lack of resources with word vector space alignment techniques: We train the NVC model on a high resource language and test it with vectors from an aligned word vector space of another language, without retraining or fine-tuning. A quantitative and qualitative analysis shows that the NVC model indeed activates meaningful concepts for unseen vectors from the aligned vector space. NVC thus becomes available for low resource languages for which no appropriate concept ground truth exists.


Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence