Summary of A Novel Dependency Framework For Enhancing Discourse Data Analysis, by Kun Sun et al.
A Novel Dependency Framework for Enhancing Discourse Data Analysis
by Kun Sun, Rong Wang
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The development of various theories of discourse structure has led to the creation of discourse corpora based on these theories. However, the existence of discourse corpora established on different theoretical bases creates challenges when exploring them consistently. This study focuses on converting PDTB annotations into dependency structures using refined BERT-based discourse parsers. The study tests the validity of the derived dependency data in English, Chinese, and several other languages. Additionally, it converts both PDTB and RST annotations for the same texts into dependencies, applying “dependency distance” metrics to examine the correlation between RST dependencies and PDTB dependencies in English. The results show that PDTB dependency data is valid, with a strong correlation between the two types of dependency distances. This study presents a comprehensive approach for analyzing and evaluating discourse corpora by employing discourse dependencies for unified analysis. By applying dependency representations, it extracts data from PDTB, RST, and SDRT corpora coherently. Moreover, cross-linguistic validation establishes the framework’s generalizability beyond English. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study tries to solve a problem with how we analyze conversations. Different theories about conversation structure have led to many different datasets (collections of labeled text). This makes it hard to compare and combine these datasets. The researchers in this study want to make it easier by converting the labels from one dataset into a standard format that can be used for all languages. They tested their method on English, Chinese, and other languages, and found that it works well. They also compared their method with another way of analyzing conversations and found that they are very similar. This study’s goal is to make it easier to analyze conversations by providing a common language that can be used across different datasets. |
Keywords
» Artificial intelligence » Bert » Discourse
