Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 5th International Conference on Natural Language and Speech Processing |
| Subtitle of host publication | ICNLSP 2022 |
| Editors | Mourad Abbas, Abed Alhakim Freihat |
| Pages | 282-287 |
| Number of pages | 6 |
| ISBN (electronic) | 9781959429364 |
| Publication status | Published - 2022 |
| Event | 5th International Conference on Natural Language and Speech Processing, ICNLSP 2022 - Virtual, Online Duration: 16 Dec 2022 → 17 Dec 2022 |
Abstract
We present a cross-lingual approach for the extraction of Vossian Antonomasia, a stylistic device especially popular in newspaper articles. We evaluate a zero-shot transfer learning approach and two approaches that use machine-translated training and test data. We show that our proposed models achieve strong results on all test datasets in the target language. As annotated data is sparse, especially in the target language, we generate additional test data to evaluate our models and conclude with a robustness study on real-world data.
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Signal Processing
- Social Sciences(all)
- Linguistics and Language
- Social Sciences(all)
- Communication
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Proceedings of the 5th International Conference on Natural Language and Speech Processing: ICNLSP 2022. ed. / Mourad Abbas; Abed Alhakim Freihat. 2022. p. 282-287.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
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TY - GEN
T1 - “Der Frank Sinatra der Wettervorhersage”
T2 - 5th International Conference on Natural Language and Speech Processing, ICNLSP 2022
AU - Schwab, Michel
AU - Jäschke, Robert
AU - Fischer, Frank
PY - 2022
Y1 - 2022
N2 - We present a cross-lingual approach for the extraction of Vossian Antonomasia, a stylistic device especially popular in newspaper articles. We evaluate a zero-shot transfer learning approach and two approaches that use machine-translated training and test data. We show that our proposed models achieve strong results on all test datasets in the target language. As annotated data is sparse, especially in the target language, we generate additional test data to evaluate our models and conclude with a robustness study on real-world data.
AB - We present a cross-lingual approach for the extraction of Vossian Antonomasia, a stylistic device especially popular in newspaper articles. We evaluate a zero-shot transfer learning approach and two approaches that use machine-translated training and test data. We show that our proposed models achieve strong results on all test datasets in the target language. As annotated data is sparse, especially in the target language, we generate additional test data to evaluate our models and conclude with a robustness study on real-world data.
UR - http://www.scopus.com/inward/record.url?scp=85152141836&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85152141836
SP - 282
EP - 287
BT - Proceedings of the 5th International Conference on Natural Language and Speech Processing
A2 - Abbas, Mourad
A2 - Freihat, Abed Alhakim
Y2 - 16 December 2022 through 17 December 2022
ER -