aiqbench analysis
The Multilingual FrameNet Corpus (mFNC) represents a significant advancement in frame semantic parsing by harmonizing data across ten languages, directly addressing the critical need for robust multilingual training datasets. By demonstrating consistent performance improvements over existing state-of-the-art models in cross-lingual settings, this resource provides essential infrastructure for developing more inclusive and globally applicable natural language understanding systems.
The Multilingual FrameNet Corpus
arXiv:2608.23037v1 Announce Type: cross Abstract: This paper introduces the Multilingual FrameNet Corpus (mFNC), a novel resource that extends the English Berkeley FrameNet corpus by collecting and harmonizing existing language-specific corpora across nine additional languages: Brazilian Portuguese, Chinese, Dutch, French, German, Italian, Korean, Latvian and Swedish. By training models that rely on different architectures on the mFNC, we consistently outperform existing state-of-the-art Frame Semantic Parsers in both multilingual and cross-lingual settings, underscoring the importance of multilingual training data. The mFNC and our trained FSP models are openly available at https://github.com/beatrice-f/mFNC.
Abstract:This paper introduces the Multilingual FrameNet Corpus (mFNC), a novel resource that extends the English Berkeley FrameNet corpus by collecting and harmonizing existing language-specific corpora across nine additional languages: Brazilian Portuguese, Chinese, Dutch, French, German, Italian, Korean, Latvian and Swedish. By training models that rely on different architectures on the mFNC, we consistently outperform existing state-of-the-art Frame Semantic Parsers in both multilingual and cross-lingual settings, underscoring the importance of multilingual training data. The mFNC and our trained FSP models are openly available at this https URL.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2608.23037 [cs.CL]
(or arXiv:2608.23037v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.23037 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 184 words
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