Overview of the SPMRL 2013 Shared Task: A Cross-Framework Evaluation of Parsing Morphologically Rich Languages
Summary
The paper presents the inaugural shared task devoted to statistically parsing morphologically rich languages (MRLs), organized alongside the 4th SPMRL workshop at EMNLP 2013. Earlier cross-lingual parsing campaigns, such as the CoNLL dependency tasks, used mismatched data sizes and assumed morphology was already known. That made it hard to see why parsers struggle on languages where grammatical relations are encoded inside words.
The organizers built treebanks for nine typologically diverse languages, each in both constituency and dependency form, in two sizes: full training data and a 5,000-sentence subset. They defined three input scenarios:
- gold morphology,
- automatically predicted morphology,
- raw input supplied as a lattice of possible analyses.
They also proposed evaluation that works across frameworks and scenarios.
Seven teams entered more than 14 systems. The strongest were mostly ensembles or pipelines built on a strong morphological analyzer. Ambiguous morphology remained hard for every system. The work matters because it created a stable benchmark and baselines for realistic MRL parsing.
Key points
- Data: nine languages, all in both constituency and dependency annotation. Resources included the Arabic Penn Treebank (still distributed via LDC), the Hebrew treebank with its morphological analyzer, the French treebank and the Kaist Korean Treebank.
- Training regimes: a full setup and a small 5,000-sentence setup. Some cross-framework comparisons use unlabeled TedEval on generalized trees, with training on 5k sentences and testing on 5k terminals.
- Input scenarios: gold, predicted and raw (lattice). This is the first campaign to report parser results on realistic, morphologically ambiguous input.
- Overall findings: more data helps, and gold morphology gives clearly higher scores than predicted input. The gap between dependency and constituency parsing is smaller than often assumed; the learning model and label granularity matter more.
- Hardest languages: Semitic and Asian languages remain the hardest to parse whatever the method. Whether this comes from the language, the models or treebank consistency stays open.
Interesting ideas
- I1. Lattice input for joint disambiguation. The parser receives a lattice of candidate morphological analyses and chooses segmentation together with syntax, as ALPAGE:DYALOG did. This could help any language where tokens hide several syntactic units, such as clitics or compounds, and avoids passing early tagging errors down the pipeline.
- I2. Gold vs. predicted vs. raw scenarios. Reporting all three separates a lab-condition upper bound from real pipeline performance. This makes clear how much of the error comes from upstream morphology.
- I3. Fixed small-data track. A training subset of the same size across languages makes treebanks of very different sizes comparable. It also shows how parsers behave under resource scarcity, which is relevant for low-resource treebanks.
- I4. Cross-framework evaluation on generalized trees. Scoring with a metric like TedEval, which works on tree structures regardless of formalism, lets constituency and dependency outputs be compared fairly. This is useful whenever competing systems produce different output representations.
- I5. Combining parsers as a robust baseline. Ensembles of different parsers or training runs gave stable results across languages. They are a sensible default before investing in language-specific engineering.
Cited by
- AlephBERT: Language Model Pre-training and Evaluation from Sub-Word to Sentence Level
- It Depends: Dependency Parser Comparison Using A Web-based Evaluation Tool
- Universal Dependencies for Croatian (that work for Serbian, too)
- Developing a French FrameNet: Methodology and First results
- Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All
- Constituency Parsing with a Self-Attentive Encoder
- Multilingual Constituency Parsing with Self-Attention and Pre-Training
- AlephBERT: A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With
- When Similar Means Different: Evaluating LLMs on Arabic–Hebrew Cognates
- Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment
- A Graph-based Lattice Dependency Parser for Joint Morphological Segmentation and Syntactic Analysis
- Neural Modeling for Named Entities and Morphology (NEMO2)
- Universal Joint Morph-Syntactic Processing: The Open University of Israel's Submission to The CoNLL 2017 Shared Task
- CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
- Universal Morpho-Syntactic Parsing and the Contribution of Lexica: Analyzing the ONLP Lab Submission to the CoNLL 2018 Shared Task
- ParaShoot: A Hebrew Question Answering Dataset
Cite
@inproceedings{seddah2013overview,
title = {{Overview of the SPMRL 2013 Shared Task: A Cross-Framework Evaluation of Parsing Morphologically Rich Languages}},
author = {Seddah, Djamé and Tsarfaty, Reut and Kübler, Sandra and Candito, Marie and Choi, Jinho D. and Farkas, Richárd and Foster, Jennifer and Goenaga, Iakes and Galletebeitia, Koldo Gojenola and Goldberg, Yoav and Green, Spence and Habash, Nizar and Kuhlmann, Marco and Maier, Wolfgang and Nivre, Joakim and Przepiórkowski, Adam and Roth, Ryan and Seeker, Wolfgang and Versley, Yannick and Vincze, Veronika and Woliński, Marcin and Wróblewska, Alina and Clergerie, Eric Villemonte de la},
booktitle = {Proceedings of the Fourth Workshop on Statistical Parsing of Morphologically-Rich Languages},
year = {2013},
doi = {10.18653/v1/w13-4917},
url = {https://doi.org/10.18653/v1/w13-4917}
}References
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