|
View: |
Part 1: Document Description
|
|
Citation |
|
|---|---|
|
Title: |
Encoder-Decoder Model for Semantic Role Labeling |
|
Identification Number: |
doi:10.11588/data/TOI9NQ |
|
Distributor: |
heiDATA |
|
Date of Distribution: |
2020-01-23 |
|
Version: |
1 |
|
Bibliographic Citation: |
Daza, Angel, 2020, "Encoder-Decoder Model for Semantic Role Labeling", https://doi.org/10.11588/data/TOI9NQ, heiDATA, V1 |
|
Citation |
|
|
Title: |
Encoder-Decoder Model for Semantic Role Labeling |
|
Identification Number: |
doi:10.11588/data/TOI9NQ |
|
Authoring Entity: |
Daza, Angel (Leibniz Institute for the German Language) |
|
Date of Production: |
2019 |
|
Distributor: |
heiDATA |
|
Access Authority: |
Daza, Angel |
|
Holdings Information: |
https://doi.org/10.11588/data/TOI9NQ |
|
Study Scope |
|
|
Keywords: |
Arts and Humanities, Computer and Information Science, Semantic Role Labeling, SRL, monolingual setting, multilingual setting, cross-lingual setting, semantic role annotation |
|
Topic Classification: |
Semantic Role Labeling |
|
Abstract: |
<p><strong>Abstract (Daza & Frank 2019):</strong></p> <p>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.</p> |
|
Kind of Data: |
program source code |
|
Methodology and Processing |
|
|
Sources Statement |
|
|
Data Access |
|
|
Other Study Description Materials |
|
|
Related Publications |
|
|
Citation |
|
|
Title: |
<p>Daza, Angel and Frank, Anette (2019). Translate and label! An encoder-decoder approach for cross-lingual semantic role labeling. In <em>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing</em>, November 3-7, 2019, Hong Kong, China.</p> |
|
Identification Number: |
1908.11326 |
|
Bibliographic Citation: |
<p>Daza, Angel and Frank, Anette (2019). Translate and label! An encoder-decoder approach for cross-lingual semantic role labeling. In <em>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing</em>, November 3-7, 2019, Hong Kong, China.</p> |
|
Label: |
README.md |
|
Notes: |
text/markdown |
|
Label: |
SRL-S2S.zip |
|
Notes: |
application/zip |