📄 Joint Transition-Based Models for Morpho-Syntactic Parsing: Parsing Strategies for MRLs and a Case Study from Modern Hebrew

What it is about

In Hebrew, morphological analysis and disambiguation (MA&D) normally comes before parsing, yet the syntax would help choose the morphology, and the reverse. The authors merge a morphological transition system (More and Tsarfaty 2016) with two dependency systems (Arc-Standard and Arc-ZEager) into one transition-based parser, with one score, joint training and beam search (beam 64). They compare three strategies: a pipeline; MDFirst, where the parser sees all beam candidates of the morphology; and ArcGreedy k, where syntax moves ahead once k morphemes are in its buffer. An ENDTOKEN transition handles lattice paths of different lengths. On the Modern Hebrew SPMRL data, with gold paths infused into the lattices, morphological F1 rises from about 93.3 (standalone) to about 94.4–94.7 under the joint strategies, while dependency F1 drops by up to 0.5 point. Arc-ZEager is slightly better than Arc-Standard. The joint system beats earlier Hebrew parsers end to end.

Ideas

• I2. Index tree nodes by token boundary, not by lattice arc, so that segmentations of different length do not shift the indices. An ENDTOKEN-like step does this; the same problem appears in form-based arc matching.

For Mova

• I1. MDFirst as a re-ranker: keep the top-B segmentations from the Hebrew segmenter and let the parser rescore each one with its tree score, then take the best joint result. The first target is the segmenter in tools/hbo.
• I3. Evaluate with form-based arcs: a dependency counts as correct when the head and dependent forms match, not their positions. The paper's example shows that a single wrong segment otherwise zeroes the whole sentence. Put this in the tools/hbo evaluation.

I1 goes into tools/hbo (segmenter to gparse) and languages/hbo/13-mova-tools.md. I2 and I3 go into the tools/hbo evaluation and languages/he/11-ud-analysis.md. The paper is CC BY 4.0, so a short phrase may be quoted with its page; no sentences are copied.

🏛️ Institutions

🎓Open University of IsraelRaanana · Israel13 works · 2,012 citations

Topics🧩 Morphology🕎 Hebrew & Aramaic🐪 Semitic languages📐 Syntax & grammar formalisms

Cite

Cite as Amir More, Amit Seker, Victoria Basmova and Reut Tsarfaty. 2019. Joint Transition-Based Models for Morpho-Syntactic Parsing: Parsing Strategies for MRLs and a Case Study from Modern Hebrew. Transactions of the Association for Computational Linguistics.

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.1162/tacl_a_00253

@article{more2019joint,
  title = {{Joint Transition-Based Models for Morpho-Syntactic Parsing: Parsing Strategies for MRLs and a Case Study from Modern Hebrew}},
  author = {Amir More, Amit Seker, Victoria Basmova and Reut Tsarfaty},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2019},
  doi = {10.1162/tacl\_a\_00253},
  url = {https://doi.org/10.1162/tacl_a_00253}
}

References

4 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Steven P. Abney and Mark Johnson. 1991. Memory requirements and local ambiguities of parsing strategies. Journal of Psycholinguistic Research, 20(3):233–250. find
2. Meni Adler. 2007. Hebrew Morphological Disambiguation: An Unsupervised Stochastic Word-Based Approach. Ph.D. thesis, Ben-Gurion University of the Negev, Israel. find
3. Meni Adler and Michael Elhadad. 2006. An unsupervised morpheme-based HMM for Hebrew morphological disambiguation. In Proceedings of ACL. Association for Computer Linguistics. our card
4. Daniel Andor, Chris Alberti, David Weiss, Aliaksei Severyn, Alessandro Presta, Kuzman Ganchev, Slav Petrov, and Michael Collins. 2016. Globally normalized transition-based neural networks. In Proceedings of the ACL. Association for Computational Linguistics. our card

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