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Abstract (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.
"},"author":{"citation:authorName":"Daza, Angel","citation:authorAffiliation":"Leibniz Institute for the German Language"},"citation:datasetContact":{"citation:datasetContactName":"Daza, Angel","citation:datasetContactAffiliation":"Leibniz Institute for the German Language","citation:datasetContactEmail":"daza@cl.uni-heidelberg.de"},"publication":{"publicationCitation":"Daza, Angel and Frank, Anette (2019). Translate and label! An encoder-decoder approach for cross-lingual semantic role labeling. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, November 3-7, 2019, Hong Kong, China.
","publicationIDType":"arXiv","publicationIDNumber":"1908.11326","publicationURL":"https://arxiv.org/abs/1908.11326"},"title":"Encoder-Decoder Model for Semantic Role Labeling","subject":["Arts and Humanities","Computer and Information Science"],"citation:topicClassification":{"citation:topicClassValue":"Semantic Role Labeling"},"citation:productionPlace":"Leibniz Institute for the German Language","alternativeURL":"https://github.com/Heidelberg-NLP/SRL-S2S","citation:productionDate":"2019","citation:keyword":[{"citation:keywordValue":"Semantic Role Labeling"},{"citation:keywordValue":"SRL"},{"citation:keywordValue":"monolingual setting"},{"citation:keywordValue":"multilingual setting"},{"citation:keywordValue":"cross-lingual setting"},{"citation:keywordValue":"semantic role annotation"}],"kindOfData":"program source code","@id":"https://doi.org/10.11588/data/TOI9NQ","@type":["ore:Aggregation","schema:Dataset"],"schema:version":"1.0","schema:name":"Encoder-Decoder Model for Semantic Role Labeling","schema:dateModified":"Thu Jan 23 11:33:34 CET 2020","schema:datePublished":"2020-01-23","schema:creativeWorkStatus":"RELEASED","dvcore:termsOfUse":"Licensed under General Public License v3 (GPL v3). \r\n\r\nPlease note the licenses of required components:
\r\nData publications of the Leibniz ScienceCampus “Empirical Linguistics and Computational Language Modeling”
\r\nThe Leibniz ScienceCampus “Empirical Linguistics and Computational Language Modeling” (LiMo) is a cooperative research project between the Leibniz Institute for the German Language (Leibniz-Institut für Deutsche Sprache, IDS) in Mannheim and the Department of Computational Linguistics at Heidelberg University (ICL). The general aims of the project are to develop new methods, models, and tools for compiling and analysing automatically large German textual corpora covering different domains, genres and language varieties.
\r\nThe project is supported by funds from the Baden-Württemberg Ministry of Science, Research and the Arts and the Leibniz Association together with funds provided by the Leibniz Institute for the German Language and Heidelberg University.
\r\nFunding Period: 2015 – 2020
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