{"id":3148,"identifier":"data/TOI9NQ","persistentUrl":"https://doi.org/10.11588/data/TOI9NQ","protocol":"doi","authority":"10.11588","publisher":"heiDATA","publicationDate":"2020-01-23","storageIdentifier":"file://10.11588/data/TOI9NQ","datasetVersion":{"id":474,"datasetId":3148,"datasetPersistentId":"doi:10.11588/data/TOI9NQ","storageIdentifier":"file://10.11588/data/TOI9NQ","versionNumber":1,"versionMinorNumber":0,"versionState":"RELEASED","productionDate":"2019","lastUpdateTime":"2020-01-23T10:33:34Z","releaseTime":"2020-01-23T10:33:34Z","createTime":"2019-11-20T13:13:24Z","publicationDate":"2020-01-23","citationDate":"2020-01-23","termsOfUse":"Licensed under General Public License v3 (GPL v3). \r\n\r\n
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\r\nAbstract (Daza & Frank 2019):
\r\nWe propose a Cross-lingual Encoder-Decoder model that simultaneously translates and generates sentences with Semantic Role Labeling annotations in a resource-poor target language. Unlike annotation projection techniques, our model does not need parallel data during inference time. Our approach can be applied in monolingual, multilingual and cross-lingual settings and is able to produce dependency-based and span-based SRL annotations. We benchmark the labeling performance of our model in different monolingual and multilingual settings using well-known SRL datasets. We then train our model in a cross-lingual setting to generate new SRL labeled data. Finally, we measure the effectiveness of our method by using the generated data to augment the training basis for resource-poor languages and perform manual evaluation to show that it produces high-quality sentences and assigns accurate semantic role annotations. Our proposed architecture offers a flexible method for leveraging SRL data in multiple languages.
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