SYNTOLOGY HomeExplorerAtlasCodeMethodologyAboutDevelopersFeedPricing
Paper · 2411.00491 · EMNLP · 2024

GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains

Yang Liu, Tatsuya Aoyama, Amir Zeldes, Yilun Zhu, Shabnam Behzad, Jessica Lin, Lauren Levine, Wesley Scivetti, Devika Tiwari

arXiv · PDF · Open in the Atlas

Code that ran

We have not lifted any functions out of this paper's repositories yet, so there is nothing we have run. If it links a repository, it is listed below.

Abstract

Work on shallow discourse parsing in English has focused on the Wall Street Journal corpus, the only large-scale dataset for the language in the PDTB framework. However, the data is not openly available, is restricted to the news domain, and is by now 35 years old. In this paper, we present and evaluate a new openaccess, multi-genre benchmark for PDTB-style shallow discourse parsing, based on the existing UD English GUM corpus, for which discourse relation annotations in other frameworks already exist. In a series of experiments on cross-domain relation classification, we show that while our dataset is compatible with PDTB, substantial out-of-domain degradation is observed, which can be alleviated by joint training on both datasets.

For agents

The same record, over MCP at https://syntology.ai/mcp:

get_harvested_code_for_paper("2411.00491")
get_code_for_paper("2411.00491")
have("2411.00491")

Connect an agent — have() is free.