X-SRL: Parallel Cross-lingual Semantic Role Labeling

Item Name: X-SRL: Parallel Cross-lingual Semantic Role Labeling
Author(s): Angel Daza, Anette Frank
LDC Catalog No.: LDC2021T09
ISBN: 1-58563-962-1
ISLRN: 416-358-951-021-2
DOI: https://doi.org/10.35111/10zk-gq05
Release Date: April 15, 2021
Member Year(s): 2021
DCMI Type(s): Text
Data Source(s): newswire
Application(s): machine translation, semantic role labelling
Language(s): German, Spanish, French
Language ID(s): deu, spa, fra
License(s): LDC User Agreement for Non-Members
Online Documentation: LDC2021T09 Documents
Licensing Instructions: Subscription & Standard Members, and Non-Members
Citation: Daza, Angel, and Anette Frank. X-SRL: Parallel Cross-lingual Semantic Role Labeling LDC2021T09. Web Download. Philadelphia: Linguistic Data Consortium, 2021.
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X-SRL: Parallel Cross-lingual Semantic Role Labeling was developed by Heidelberg University, Department of Computational Linguistics and the Leibniz Institute for the German Language (IDS). It consists of approximately three million words of German, French and Spanish annotated for semantic role labeling. The texts are translations of the English portion of 2009 CoNLL Shared Task Part 2 (LDC2012T04). All sentences have annotations for verbal predicates and share the original English Propbank label set across the four languages.


The 2009 CoNLL Shared Task developed syntactic dependency annotations, including the semantic dependency model roles of both verbal and nominal predicates. The following English data was used in the shared task:

For X-SRL, the English source data was automatically translated using DeepL. Automatic tokenization, lemmatization, part-of-speech tagging and syntactic parsing were then applied to the text. The data was divided into train, development and test partitions. Semantic labels were transferred for the train and development sections, and the test sentences were validated for translation quality, alignment, label transfer, and filtering.

More information on the development process and tools used is available in the included documentation.

Annotated data is in the Universal CoNLL format and encoded in UTF-8.


The creation of this corpus was funded by the Leibniz ScienceCampus "Empirical Linguistics and Computational Language Modeling" supported by Leibniz Association (grant no. SAS2015-IDS-LWC) and by the Ministry of Science, Research, and Art of Baden-Wurttemberg.


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