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This paper introduces a method for automatically expanding WordNet-style lexical resources to new languages by projecting English synsets onto aligned target-language tokens using a bilingual dictionary-augmented aligner. The method leverages a pre-trained aligner enhanced with a bilingual dictionary to project English synsets onto aligned target-language tokens, assigning corresponding lemmas to those synsets. Experiments across multiple languages demonstrate that this project-and-filter strategy improves precision compared to existing methods and baselines, while maintaining interpretability and minimizing reliance on external resources.
Ditch the LLM tax for cross-lingual lexicon expansion: this dictionary-augmented alignment method rivals LLMs in precision while staying interpretable and resource-light.
We study the task of automatically expanding WordNet-style lexical resources to new languages through sense generation. We generate senses by associating target-language lemmas with existing lexical concepts via semantic projection. Given a sense-tagged English corpus and its translation, our method projects English synsets onto aligned target-language tokens and assigns the corresponding lemmas to those synsets. To generate these alignments and ensure their quality, we augment a pre-trained base aligner with a bilingual dictionary, which is also used to filter out incorrect sense projections. We evaluate the method on multiple languages, comparing it to prior methods, as well as dictionary-based and large language model baselines. Results show that the proposed project-and-filter strategy improves precision while remaining interpretable and requiring few external resources. We plan to make our code, documentation, and generated sense inventories accessible.