Assessing cross-lingual frame stability and identifiability with multilingual neural embeddings

Frame semantics (Fillmore 1976) is a framework for the analysis of natural language semantics in terms of conceptual structures which can provide a semantic basis to constructions. Its cross-lingual applicability is subject to debate. Our study leverages recent developments in computational linguistics to assess the cross-lingual applicability of frames as defined in the FrameNet framework. Using a pre-trained multilingual model fine-tuned on frame identification, we test if frames have comparable representations in different languages and if we can identify frames cross-lingually using only English annotated examples. We show, using data from German and Korean, that frames are generally cross-lingually stable and identifiable; cross-lingual differences in lexicalization patterns are the biggest obstacle to cross-lingual application.