A Survey of Unsupervised Dependency Parsing (2020)

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.

## 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 always 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 syntactically annotated training sentences, including transfer learning, unsupervised learning, and semisupervised learning. among these directions, unsupervised learning of dependency parsers (a.k.a. unsupervised 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 unlimited 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 parsing @xcite . 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. ## conclusion in this paper, we present a survey on the current advances of unsupervised dependency parsing. we first motivate the importance of the unsupervised dependency parsing task and discuss several related research areas. we split existing approaches into two main categories, and explain each category in detail. besides, we discuss several recent trends in this research area. while there is a growing body of work that improves unsupervised dependency parsing, its performance is still below that of supervised dependency parsing by a large margin. this suggests that more investigation and research are needed to make unsupervised parsers useful for real applications. we hope that our survey can promote further development in this research direction.