Transferring Semantic Roles Using Translation and Syntactic Information
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Abstract (arXiv)
Our paper addresses the problem of annotation projection for semantic role labeling for resource-poor languages using supervised annotations from a resource-rich language through parallel data. We propose a transfer method that employs information from source and target syntactic dependencies as well as word alignment density to improve the quality of an iterative bootstrapping method. Our experiments yield a 3.5 absolute labeled F-score improvement over a standard annotation projection method.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Oct 2017.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 0; 2025: 0; 2026: 1.
7 papers.
- 2022Frustratingly Easy Label Projection for Cross-lingual Transfercited by 33
- 2021Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extractioncited by 16
- 2023Contextual Label Projection for Cross-Lingual Structured Predictioncited by 9
- 2023MultiTACRED: A Multilingual Version of the TAC Relation Extraction Datasetcited by 5
- 2022Word Alignment in the Era of Deep Learning: A Tutorialcited by 3
- 2023Factuality Detection using Machine Translation -- a Use Case for German Clinical Textcited by 0
- 2026Just Use XML: Revisiting Joint Translation and Label Projectioncited by 0
- 2026Just Use XML: Revisiting Joint Translation and Label Projection12 Mar 2026
- 2023Contextual Label Projection for Cross-Lingual Structured Prediction16 Sep 2023
- 2023Factuality Detection using Machine Translation -- a Use Case for German Clinical Text17 Aug 2023
- 2023MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset8 May 2023
- 2022Word Alignment in the Era of Deep Learning: A Tutorial30 Nov 2022
- 2022Frustratingly Easy Label Projection for Cross-lingual Transfer28 Nov 2022
- 2021Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction14 Sep 2021