Coordinate Constructions in English Enhanced Universal Dependencies: Analysis and Computational Modeling
Summary
In Enhanced UD, coordination is represented by propagating the first conjunct's relations (its head and its dependents) to the other conjuncts. The English enhanced treebanks were produced automatically, by applying the heuristic converter of Schuster and Manning (2016) to manual basic trees, and that converter only propagates core arguments. The authors manually checked and corrected 1,417 EWT sentences with coordinated verbs. They decided by meaning, so they also propagated adjunct dependents such as obl where the context required it. During annotation they found systematic converter errors, for example when conjuncts differ in voice or mood, or when several coordinations interact. With the new data they compare rules, SVM classifiers and a new neural model with tree features and RoBERTa, for the first time systematically. On gold trees the data-driven models beat the rules. For automatic parsing, a graph parser that predicts enhanced edges directly clearly beats "tree parser plus converter" pipelines.
How Mova uses it
- Coordination: the head is the first conjunct, cc is on the next one: cited in the seed on UD v2 coordination (the first conjunct is the head, and
ccattaches to the following conjunct) for the observation that the Enhanced UD converter fails systematically when conjuncts differ in voice or mood. - The seed's actual rules,
en.errors.conj-rightwardanden.errors.cc-before-conjunct, come from the UD guidelines and validator. This paper only adds a caveat about enhanced-graph conversion, which Mova does not produce yet.
Effectiveness in Mova
Background reading, a single supporting citation in one seed. Not measured separately.
👨🔬💥
Cite
@inproceedings{grunewald2021coordinate,
title = {{Coordinate Constructions in English Enhanced Universal Dependencies: Analysis and Computational Modeling}},
author = {Grünewald, Stefan and Piccirilli, Prisca and Friedrich, Annemarie},
booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume},
year = {2021},
doi = {10.18653/v1/2021.eacl-main.67},
url = {https://doi.org/10.18653/v1/2021.eacl-main.67}
}References
- Chiara Alzetta, Felice Dell’Orletta, Simonetta Montemagni, Maria Simi, and Giulia Venturi. 2018. Assessing the impact of incremental error detection and correction. a case study on the Italian universal dependency treebank. In Proceedings of the Second Workshop on Universal Dependencies (UDW 2018), pages 1–7, Brussels, Belgium. Association for Computational Linguistics. find
- Gosse Bouma, Djamé Seddah, and Daniel Zeman. 2020. Overview of the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies. In Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies, Seattle, US. Association for Computational Linguistics. find our card
- Mathieu Dehouck, Mark Anderson, and Carlos Gómez-Rodríguez. 2020. Efficient EUD parsing. In Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies, pages 192–205, Online. Association for Computational Linguistics. find
- Timothy Dozat and Christopher D. Manning. 2017. Deep biaffine attention for neural dependency parsing. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. Open-Review.net. find our card
- Timothy Dozat and Christopher D. Manning. 2018. Simpler but more accurate semantic dependency parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 484–490, Melbourne, Australia. Association for Computational Linguistics. find
- Stefan Grünewald and Annemarie Friedrich. 2020. RobertNLP at the IWPT 2020 shared task: Surprisingly simple enhanced UD parsing for English. In Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies, pages 245–252, Online. Association for Computational Linguistics. find our card
- Han He and Jinho D. Choi. 2020. Adaptation of multilingual transformer encoder for robust enhanced Universal Dependency parsing. In Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies, pages 181– 191, Online. Association for Computational Linguistics. find
- Johannes Heinecke. 2019. ConlluEditor: a fully graphical editor for universal dependencies treebank files. In Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019), pages 87–93, Paris, France. Association for Computational Linguistics. find
- Johannes Heinecke. 2020. Hybrid enhanced Universal Dependencies parsing. In Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies, pages 174–180, Online. Association for Computational Linguistics. find our card
- Daniel Hershcovich, Miryam de Lhoneux, Artur Kulmizev, Elham Pejhan, and Joakim Nivre. 2020. Køpsala: Transition-based graph parsing via efficient training and effective encoding. In Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies, pages 236– 244, Online. Association for Computational Linguistics. find
- Jenna Kanerva, Filip Ginter, and Sampo Pyysalo. 2020. Turku enhanced parser pipeline: From raw text to enhanced graphs in the IWPT 2020 shared task. In Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies, pages 162–173, Online. Association for Computational Linguistics. find
- Dan Kondratyuk and Milan Straka. 2019. 75 languages, 1 model: Parsing universal dependencies universally. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2779–2795, Hong Kong, China. Association for Computational Linguistics. find our card
- Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692. ↗
- Ilya Loshchilov and Frank Hutter. 2017. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101. ↗
- Marie-Catherine de Marneffe, Timothy Dozat, Natalia Silveira, Katri Haverinen, Filip Ginter, Joakim Nivre, and Christopher D. Manning. 2014. Universal Stanford dependencies: A cross-linguistic typology. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), pages 4585–4592, Reykjavik, Iceland. European Language Resources Association (ELRA). find our card
- Marie-Catherine de Marneffe, Bill MacCartney, and Christopher D. Manning. 2006. Generating typed dependency parses from phrase structure parses. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), Genoa, Italy. European Language Resources Association (ELRA). find our card
- Jonathan May and Jay Priyadarshi. 2017. SemEval-2017 task 9: Abstract Meaning Representation parsing and generation. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 536–545, Vancouver, Canada. Association for Computational Linguistics. find
- Skatje Myers and Martha Palmer. 2019. ClearTAC: Verb tense, aspect, and form classification using neural nets. In Proceedings of the First International Workshop on Designing Meaning Representations, pages 136–140, Florence, Italy. Association for Computational Linguistics. 2017: Downstream evaluation of graph-based dependency representations. In Proceedings of the 2017 Shared Task on Extrinsic Parser Evaluation (EPE 2017), pages 47–59. find
- Joakim Nivre, Paola Marongiu, Filip Ginter, Jenna Kanerva, Simonetta Montemagni, Sebastian Schuster, and Maria Simi. 2018. Enhancing universal dependency treebanks: A case study. In Proceedings of the Second Workshop on Universal Dependencies (UDW 2018), pages 102–107, Brussels, Belgium. Association for Computational Linguistics. find our card
- Sebastian Schuster and Christopher D. Manning. 2016. Enhanced English universal dependencies: An improved representation for natural language understanding tasks. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 2371–2378, Portorož, Slovenia. European Language Resources Association (ELRA). find our card
- Jenna Nyblom, Samuel Kohonen, Katri Haverinen, Tapio Salakoski, and Filip Ginter. 2013. Predicting conjunct propagation and other extended Stanford dependencies. In Proceedings of the Second International Conference on Dependency Linguistics (DepLing 2013), pages 252–261, Prague, Czech Republic. Charles University in Prague, Matfyzpress, Prague, Czech Republic. find our card
- Stephan Oepen, Marco Kuhlmann, Yusuke Miyao, Daniel Zeman, Silvie Cinková, Dan Flickinger, Jan Hajič, and Zdeňka Urešová. 2015. SemEval 2015 task 18: Broad-coverage semantic dependency parsing. In Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 915–926, Denver, Colorado. Association for Computational Linguistics. find
- Stephan Oepen, Marco Kuhlmann, Yusuke Miyao, Daniel Zeman, Dan Flickinger, Jan Hajič, Angelina Ivanova, and Yi Zhang. 2014. SemEval 2014 task 8: Broad-coverage semantic dependency parsing. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 63–72, Dublin, Ireland. Association for Computational Linguistics. find
- Peng Qi, Timothy Dozat, Yuhao Zhang, and Christopher D. Manning. 2018. Universal dependency parsing from scratch. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 160–170, Brussels, Belgium. Association for Computational Linguistics. find our card
- Anita Ramm, Sharid Loáiciga, Annemarie Friedrich, and Alexander Fraser. 2017. Annotating tense, mood and voice for English, French and German. In Proceedings of ACL 2017, System Demonstrations, pages 1–6, Vancouver, Canada. Association for Computational Linguistics. find our card
- Swarnadeep Saha, Yixin Nie, and Mohit Bansal. 2020. ConjNLI: Natural language inference over conjunctive sentences. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8240–8252, Online. Association for Computational Linguistics. find
- Sebastian Schuster, Éric Villemonte de La Clergerie, Marie Candito, Benoît Sagot, Christopher Manning, and Djamé Seddah. 2017. Paris and Stanford at EPE Sebastian Schuster, Joakim Nivre, and Christopher D. Manning. 2018. Sentences with gapping: Parsing and reconstructing elided predicates. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1156–1168, New Orleans, Louisiana. Association for Computational Linguistics. find our card
- Maria Simi and Simonetta Montemagni. 2018. Bootstrapping enhanced universal dependencies for italian. In 5th Italian Conference on Computational Linguistics, CLiC-it 2018, volume 2253. CEUR-WS. find
- Ian Tenney, Dipanjan Das, and Ellie Pavlick. 2019. BERT rediscovers the classical NLP pipeline. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4593– 4601, Florence, Italy. Association for Computational Linguistics. find
- Xinyu Wang, Yong Jiang, and Kewei Tu. 2020. Enhanced Universal Dependency parsing with second-order inference and mixture of training data. In Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies, pages 215–220, Online. Association for Computational Linguistics. find
- Guillaume Wisniewski. 2018. Errator: a tool to help detect annotation errors in the universal dependencies project. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA). find our card
- Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier-ric Cistac, Tim Rault, R’emi Louf, Morgan Funtow-icz, and Jamie Brew. 2019. HuggingFace’s transformers: State-of-the-art natural language processing. ArXiv, abs/1910.03771. ↗
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