A Survey of Unsupervised Dependency Parsing

Proceedings of the 28th International Conference on Computational Linguistics, pages 2522–2533 Barcelona, Spain (Online), December 8-13, 2020 2522 A Survey of Unsupervised Dependency Parsing Wenjuan Han1∗, Yong Jiang2∗, Hwee Tou Ng1, Kewei Tu3† 1Department of Computer Science, National University of Singapore 2Alibaba DAMO Academy, Alibaba Group 3School of Information Science and Technology, ShanghaiTech University dcshanw@nus.edu.sg yongjiang.jy@alibaba-inc.com nght@comp.nus.edu.sg tukw@shanghaitech.edu.cn Abstract Syntactic dependency parsing is an important task in natural language processing. Unsupervised dependency parsing aims to learn a dependency parser from sentences that have no annotation of their correct parse trees. Despite its difficulty, unsupervised parsing is an interesting research direction because of its capability of utilizing almost unlimited unannotated text data. It also serves as the basis for other research in low-resource parsing. In this paper, we survey existing approaches to unsupervised dependency parsing, identify two major classes of approaches, and discuss recent trends. We hope that our survey can provide insights for researchers and facilitate future research on this topic. 1 Introduction Dependency parsing is an important task in natural language processing that aims to capture syntactic information in sentences in the form of dependency relations between words. It finds applications in semantic parsing, machine translation, relation extraction, and many other tasks. Supervised learning is the main technique used to automatically learn a dependency parser from data. It requires the training sentences to be manually annotated with their correct parse trees. Such a training dataset is called a treebank. A major challenge faced by supervised learning is that treebanks are not al- ways available for new languages or new domains and building a high-quality treebank is very expensive and time-consuming. There are multiple research directions that try to learn dependency parsers with few or even no syn- tactically annotated training sentences, including transfer learning, unsupervised learning, and semi- supervised learning. Among these directions, unsupervised learning of dependency parsers (a.k.a. unsu- pervised dependency parsing and dependency grammar induction) is the most challenging, which aims to obtain a dependency parser without using annotated sentences. Despite its difficulty, unsupervised parsing is an interesting research direction, not only because it would reveal ways to utilize almost un- limited text data without the need for human annotation, but also because it can serve as the basis for studies of transfer and semi-supervised learning of parsers. The techniques developed for unsupervised dependency parsing could also be utilized for other NLP tasks, such as unsupervised discourse pars- ing (Nishida and Nakayama, 2020). In addition, research in unsupervised parsing inspires and verifies cognitive research of human language acquisition. In this paper, we conduct a survey of unsupervised dependency parsing research. We first introduce the definition and evaluation metrics of unsupervised dependency parsing, and discuss research areas related to it. Then we present in detail two major classes of approaches to unsupervised dependency parsing: generative approaches and discriminative approaches. Finally, we discuss important new techniques and setups of unsupervised dependency parsing that appear in recent years. ∗Equal contributions. †Corresponding author. This work is licensed under a Creative Commons …

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