Cezar Sas
2020
WikiBank: Using Wikidata to Improve Multilingual Frame-Semantic Parsing
Cezar Sas
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Meriem Beloucif
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Anders Søgaard
Proceedings of the Twelfth Language Resources and Evaluation Conference
Frame-semantic annotations exist for a tiny fraction of the world’s languages, Wikidata, however, links knowledge base triples to texts in many languages, providing a common, distant supervision signal for semantic parsers. We present WikiBank, a multilingual resource of partial semantic structures that can be used to extend pre-existing resources rather than creating new man-made resources from scratch. We also integrate this form of supervision into an off-the-shelf frame-semantic parser and allow cross-lingual transfer. Using Google’s Sling architecture, we show significant improvements on the English and Spanish CoNLL 2009 datasets, whether training on the full available datasets or small subsamples thereof.
2019
X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension
Mostafa Abdou
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Cezar Sas
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Rahul Aralikatte
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Isabelle Augenstein
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Anders Søgaard
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)
Although the vast majority of knowledge bases (KBs) are heavily biased towards English, Wikipedias do cover very different topics in different languages. Exploiting this, we introduce a new multilingual dataset (X-WikiRE), framing relation extraction as a multilingual machine reading problem. We show that by leveraging this resource it is possible to robustly transfer models cross-lingually and that multilingual support significantly improves (zero-shot) relation extraction, enabling the population of low-resourced KBs from their well-populated counterparts.
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