Translate and label!: An encoder-decoder approach for cross-lingual semantic role labeling
We 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 b...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) Kapitel/Artikel |
| Sprache: | Englisch |
| Veröffentlicht: |
29 Aug 2019
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| In: |
Arxiv
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| Online-Zugang: | Verlag, Volltext: http://arxiv.org/abs/1908.11326
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| Verfasserangaben: | Angel Daza and Anette Frank |
MARC
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| 245 | 1 | 0 | |a Translate and label! |b An encoder-decoder approach for cross-lingual semantic role labeling |c Angel Daza and Anette Frank |
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| 520 | |a We 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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| 650 | 4 | |a Computer Science - Machine Learning | |
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