How Much Does Lookahead Matter for Disambiguation? Partial Arabic Diacritization Case Study
What it is about
The authors ask which short vowels a reader of unpointed Arabic really needs, and they print only those. Two neural diacritizers are trained on the Tashkeela corpus: a unidirectional LSTM that sees only the words already read, and a transformer that sees the whole sentence. Where the two disagree, the whole-sentence reading is kept and its diacritic is printed; everywhere else the letter stays bare. A new set of 75 MSA tweets, fully and partially vowelized by native readers (who kept 19–26% of the diacritics), supports the evaluation. The partial diacritizer improves downstream translation over no diacritics and over random selection. The full-sentence model reaches 3.57 DER / 8.52 WER with case endings on the Tashkeela test set, including NO DIACRITIC.
Ideas
- I1. Lookahead as disagreement: run a left-context-only analysis and a full-sentence analysis over the same text, and mark each word where they differ as "needs lookahead". This is a deterministic diff of two passes, and it names the words a left-to-right reader cannot settle.
- I2. Measure the lookahead set on unpointed Biblical Hebrew: how many MACULA tokens change segmentation or lemma when the right context is removed. It gives a number for how much unpointed text really depends on what follows.
- I3. Data rule for training gold: drop lines whose share of marked letters falls under a threshold (the paper uses 50% for MSA against 80% in earlier work). Keep the threshold as a recorded parameter, so every run can state it.
For Mova
I1 and I2 go into tools/hbo (unpointed mode), as a second pass next to the segmenter; the lookahead set is also a check in 13. Biblical Hebrew in Mova: the tools. I2 uses treebanks/hbo/ptnk as its gold. I3 is a rule for any pointed training text built for vowel restoration in tools/hbo.
Cite
@article{esmail2022how,
title = {{How Much Does Lookahead Matter for Disambiguation? Partial Arabic Diacritization Case Study}},
author = {Esmail, Saeed and Bar, Kfir and Dershowitz, Nachum},
journal = {Computational Linguistics},
year = {2022},
doi = {10.1162/coli\_a\_00456},
url = {https://doi.org/10.1162/coli_a_00456}
}References
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