Error analysis and the role of morphology
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Documents
- Error Analysis and the Role of Morphology
Final published version, 648 KB, PDF document
We evaluate two common conjectures in error analysis of NLP models: (i) Morphology is predictive of errors; and (ii) the importance of morphology increases with the morphological complexity of a language. We show across four different tasks and up to 57 languages that of these conjectures, somewhat surprisingly, only (i) is true. Using morphological features does improve error prediction across tasks; however, this effect is less pronounced with morphologically complex languages. We speculate this is because morphology is more discriminative in morphologically simple languages. Across all four tasks, case and gender are the morphological features most predictive of error.
Original language | English |
---|---|
Title of host publication | EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference |
Publisher | Association for Computational Linguistics |
Publication date | 2021 |
Pages | 1887-1900 |
ISBN (Electronic) | 9781954085022 |
Publication status | Published - 2021 |
Event | 16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021 - Virtual, Online Duration: 19 Apr 2021 → 23 Apr 2021 |
Conference
Conference | 16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021 |
---|---|
By | Virtual, Online |
Periode | 19/04/2021 → 23/04/2021 |
Sponsor | Babelscape, Bloomberg Engineering, Facebook AI, Grammarly, LegalForce |
Bibliographical note
Publisher Copyright:
© 2021 Association for Computational Linguistics
Links
- https://aclanthology.org/2021.eacl-main.162/
Final published version
ID: 283136052