A Novel Challenge Set for Hebrew Morphological Disambiguation and Diacritics Restoration
This addresses a specific issue in Hebrew NLP for researchers and developers, but it is incremental as it focuses on dataset creation and improvement within an existing domain.
The paper tackles the problem of unbalanced morphological ambiguities in Hebrew by creating a novel challenge set for homographs, showing that current state-of-the-art disambiguation performs poorly on such cases and achieving a new SOTA with an average F1 score improvement from 0.67 to 0.95.
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.