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Found 11 papers, 8 papers with code
GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains
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.
Data Checklist: On Unit-Testing Datasets with Usable Information
Model checklists (Ribeiro et al., 2020) have emerged as a useful tool for understanding the behavior of LLMs, analogous to unit-testing in software engineering.
MultiMUC: Multilingual Template Filling on MUC-4
We introduce MultiMUC, the first multilingual parallel corpus for template filling, comprising translations of the classic MUC-4 template filling benchmark into five languages: Arabic, Chinese, Farsi, Korean, and Russian.
GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation
We evaluate state-of-the-art NLP systems on GENTLE and find severe degradation for at least some genres in their performance on all tasks, which indicates GENTLE's utility as an evaluation dataset for NLP systems.
Sentence-level Feedback Generation for English Language Learners: Does Data Augmentation Help?
In this paper, we present strong baselines for the task of Feedback Comment Generation for Writing Learning.
ELQA: A Corpus of Metalinguistic Questions and Answers about English
We present ELQA, a corpus of questions and answers in and about the English language.
DisCoDisCo at the DISRPT2021 Shared Task: A System for Discourse Segmentation, Classification, and Connective Detection
This paper describes our submission to the DISRPT2021 Shared Task on Discourse Unit Segmentation, Connective Detection, and Relation Classification.
Team DoNotDistribute at SemEval-2020 Task 11: Features, Finetuning, and Data Augmentation in Neural Models for Propaganda Detection in News Articles
This paper presents our systems for SemEval 2020 Shared Task 11: Detection of Propaganda Techniques in News Articles.
AMALGUM -- A Free, Balanced, Multilayer English Web Corpus
We present a freely available, genre-balanced English web corpus totaling 4M tokens and featuring a large number of high-quality automatic annotation layers, including dependency trees, non-named entity annotations, coreference resolution, and discourse trees in Rhetorical Structure Theory.
A Cross-Genre Ensemble Approach to Robust Reddit Part of Speech Tagging
However, when these models are applied to other corpora with different genres, and especially user-generated data from the Web, we see substantial drops in performance.
