Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging

What it is about

The paper asks whether Arabic can be segmented without a language-specific morphological tool such as MADAMIRA or Farasa. It compares three data-driven units (byte-pair encoding sub-words, pure characters, and word vectors built by a character CNN) on neural machine translation with about 1.2 million training sentences, in both directions, and on POS tagging of the Arabic Treebank (483,909 words, split 80/5/15). For Arabic-to-English, BPE scores 31.1 BLEU against 30.9 for morphological segmentation; for English-to-Arabic it scores 18.0 against 18.2. Character-based models are closest on POS tagging, at 95.9% accuracy, 1.3 points below morphological segmentation. The authors also find that the NMT system works best when the source/target token ratio is near 1 or above.

Ideas

• I1. Measure, in tools/hbo, how well an unsupervised split (BPE with a fixed merge count, chosen on held-out data) agrees with the MACULA gold segmentation of Hebrew tokens. It is a control against the hand-written prefix and suffix tables (lex, pre). Report split agreement and the out-of-vocabulary rate, not only the final parse.
• I2. The paper finds that BPE cuts unknown words into known fragments that mislead the translation, and that applying BPE only to the known vocabulary helps. For tools/hbo: keep the table split for known forms, and use a plain fixed split only for forms the tables do not cover. Test it on held-out Torah tokens.
• I3. A value ablation: the gain of morphological segmentation over no segmentation (+4.6 BLEU Arabic-to-English) is a measured worth. Run the same comparison in gparse, segmented against unsegmented Hebrew input, and keep the segmenter only where it improves the parse.

For Mova

I1 and I2 go into tools/hbo (the segmenter's tables and its fallback for out-of-vocabulary forms); the measure is split agreement against MACULA. I3 is a value ablation for gparse on the output of tools/hbo. The neural models are not reused: Mova does not need an NMT system to segment.

Topics: Part-of-speech tagging · Machine translation

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.18653/v1/p17-2095

@inproceedings{sajjad2017challenging,
  title = {{Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging}},
  author = {Sajjad, Hassan and Dalvi, Fahim and Durrani, Nadir and Abdelali, Ahmed and Belinkov, Yonatan and Vogel, Stephan},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  year = {2017},
  doi = {10.18653/v1/p17-2095},
  url = {https://doi.org/10.18653/v1/p17-2095}
}

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