Adapting Unsupervised Syntactic Parsing Methodology for Discourse Dependency Parsing (2021.acl-long)
Copied to clipboard
| Challenge: | Discourse dependency parsing is a task that requires a large amount of training data, but there is little research on it. |
| Approach: | They propose to adapt unsupervised syntactic dependency parsing methods for unsupervised discourse dependency parses using unlabeled training data. |
| Outcome: | The proposed methods outperform existing methods in semi-supervised and supervised settings and outperformed existing methods. |
Similar Papers
A Survey of Unsupervised Dependency Parsing (2020.coling-main)
Copied to clipboard
| Challenge: | Syntactic dependency parsing is an important task in natural language processing . unsupervised learning of dependency parses requires training sentences to be manually annotated with their correct parse trees. |
| Approach: | They propose to survey existing approaches to unsupervised dependency parsing . they identify two major classes of approaches and discuss recent trends . |
| Outcome: | The proposed methods can be used in semantic parsing, machine translation, relation extraction, and many other tasks. |
Out-of-Domain Discourse Dependency Parsing via Bootstrapping: An Empirical Analysis on Its Effectiveness and Limitation (2022.tacl-1)
Copied to clipboard
| Challenge: | Discourse parsing accuracy degrades significantly on out-of-domain text. |
| Approach: | They propose to use bootstrapping methods to adapt modern discourse dependency parsers to out-of-domain text without additional human supervision. |
| Outcome: | The proposed methods are significantly and consistently effective for unsupervised domain adaptation of discourse dependency parsing, but the low coverage of accurately predicted pseudo labels is a bottleneck for further improvement. |
Semi-supervised Domain Adaptation for Dependency Parsing (P19-1)
Copied to clipboard
| Challenge: | Currently, most studies on cross-domain parsing focus on unsupervised domain adaptation . however, unsupervised approaches make limited progress due to the intrinsic difficulty of both domain adaptation and parse. |
| Approach: | They propose a semi-supervised domain adaptation problem for Chinese dependency parsing by using newly-annotated large-scale domain-aware datasets. |
| Outcome: | The proposed method is more effective than direct corpus concatenation and multi-task learning. |
Unsupervised Cross-Lingual Adaptation of Dependency Parsers Using CRF Autoencoders (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work on cross-lingual adaptation of dependency parsers without annotated target corpora focuses on discriminative source parser ignoring unannotated corporata . |
| Approach: | They propose to use unsupervised discriminative parsers to adapt dependency parser to unannotated target corpora without a supervised generative parsing method. |
| Outcome: | The proposed method significantly outperforms previous methods. |
Enhancing Discourse Dependency Parsing with Sentence Dependency Parsing: A Unified Generative Method Based on Code Representation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing annotation resources for Discourse Dependency Parsing tasks are limited due to their complexity and annotation schema differences. |
| Approach: | They propose a code-based unified dependency parsing method that uses code to model dependency parses under different annotation schemas. |
| Outcome: | The proposed method improves on two Chinese DDP tasks. |
Rule Augmented Unsupervised Constituency Parsing (2021.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have shown that unsupervised parsing methods do not learn meaningful semantics (not even simple grammar) |
| Approach: | They propose an approach that utilizes very generic linguistic knowledge of the language present in the form of syntactic grammar rules and is independent of the base system. |
| Outcome: | The proposed model is independent of the base system and takes advantage of syntactic grammar rules. |
Unsupervised Natural Language Parsing (Introductory Tutorial) (2021.eacl-tutorials)
Copied to clipboard
| Challenge: | Unsupervised parsing learns a syntactic parser from training sentences without parse tree annotations. |
| Approach: | This tutorial will introduce what unsupervised parsing does and how it can be useful for and beyond syntactic parse. |
| Outcome: | This paper will provide an overview of major approaches to unsupervised parsing and analyze their strengths and weaknesses. |
Unsupervised Discourse Constituency Parsing Using Viterbi EM (2020.tacl-1)
Copied to clipboard
| Challenge: | Existing studies on unsupervised discourse parsing have shown that it is expensive, time-consuming, and sometimes highly ambiguous. |
| Approach: | They propose an unsupervised parsing algorithm using Viterbi EM with a margin-based criterion and initialization methods for Viterbia training of discourse constituents based on prior knowledge of text structures. |
| Outcome: | The proposed method outperforms fully supervised parsers in terms of performance and learning of discourse constituents. |
Unsupervised Domain Adaptation Method with Semantic-Structural Alignment for Dependency Parsing (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for dependency parsing are often of the pseudo-annotation type, but they fail to consider the change of model structure for domain adaptation. |
| Approach: | They propose a method that accomplishes unsupervised cross-domain dependency parsing without using labeled data. |
| Outcome: | The proposed method achieves consistent performance improvement on CODT1 and CTB9 domains. |
Transparent Semantic Parsing with Universal Dependencies Using Graph Transformations (2022.coling-1)
Copied to clipboard
| Challenge: | Existing semantic parsers are based on deep learning, but rule-based approaches offer advantages . a drawback of neural semantic parses is that their output lacks explainability . |
| Approach: | They propose a method that maps a syntactic dependency tree to a formal meaning representation using a series of graph transformations. |
| Outcome: | The proposed method outperforms neural parsers in English, German, Italian and Dutch. |