A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration

Avi Shmidman, Joshua Guedalia, Shaltiel Shmidman, Moshe Koppel, Reut Tsarfaty

Abstract
One of the primary tasks of morphological parsers is the disambiguation of homographs. Particularly difficult are cases of unbalanced ambiguity, where one of the possible analyses is far more frequent than the others. In such cases, there may not exist sufficient examples of the minority analyses in order to properly evaluate performance, nor to train effective classifiers. In this paper we address the issue of unbalanced morphological ambiguities in Hebrew. We offer a challenge set for Hebrew homographs — the first of its kind — containing substantial attestation of each analysis of 21 Hebrew homographs. We show that the current SOTA of Hebrew disambiguation performs poorly on cases of unbalanced ambiguity. Leveraging our new dataset, we achieve a new state-of-the-art for all 21 words, improving the overall average F1 score from 0.67 to 0.95. Our resulting annotated datasets are made publicly available for further research.
Anthology ID:
2020.findings-emnlp.297
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3316–3326
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.297
DOI:
10.18653/v1/2020.findings-emnlp.297
Bibkey:
Cite (ACL):
Avi Shmidman, Joshua Guedalia, Shaltiel Shmidman, Moshe Koppel, and Reut Tsarfaty. 2020. A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3316–3326, Online. Association for Computational Linguistics.
Cite (Informal):
A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration (Shmidman et al., Findings 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp-22-attachments/2020.findings-emnlp.297.pdf
PDF Search