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Found 14 papers, 2 papers with code
DiscoGeM: A Crowdsourced Corpus of Genre-Mixed Implicit Discourse Relations
Both the corpus and the dataset can facilitate a multitude of applications and research purposes, for example to function as training data to improve the performance of automatic discourse relation parsers, as well as facilitate research into non-connective signals of discourse relations.
Label distributions help implicit discourse relation classification
Implicit discourse relations can convey more than one relation sense, but much of the research on discourse relations has focused on single relation senses.
Classification
Implicit Discourse Relation Classification
+1
Comparison of methods for explicit discourse connective identification across various domains
Existing parse methods use varying approaches to identify explicit discourse connectives, but their performance has not been consistently evaluated in comparison to each other, nor have they been evaluated consistently on text other than newspaper articles.
Establishing Annotation Quality in Multi-label Annotations
In many linguistic fields requiring annotated data, multiple interpretations of a single item are possible.
Design Choices in Crowdsourcing Discourse Relation Annotations: The Effect of Worker Selection and Training
The current contribution studies the effect of worker selection and training on the agreement on implicit relation labels between workers and gold labels, for both the DC and the QA method.
A practical perspective on connective generation
In the current contribution, we analyse whether a sophisticated connective generation module is necessary to select a connective, or whether this can be solved with simple methods (such as random choice between connectives that are known to express a given relation, or usage of a generic language model).
Semi-automatic discourse annotation in a low-resource language: Developing a connective lexicon for Nigerian Pidgin
The lexicon shows that the majority of Nigerian Pidgin connectives are borrowed from its English lexifier, but that there are also some connectives that are unique to Nigerian Pidgin.
Prompting Implicit Discourse Relation Annotation
Pre-trained large language models, such as ChatGPT, archive outstanding performance in various reasoning tasks without supervised training and were found to have outperformed crowdsourcing workers.
Classification
Implicit Discourse Relation Classification
+3
Low-Resource Cross-Lingual Adaptive Training for Nigerian Pidgin
In this work, we target on improving upon both text classification and translation of Nigerian Pidgin (Naija) by collecting a large-scale parallel English-Pidgin corpus and further propose a framework of cross-lingual adaptive training that includes both continual and task adaptive training so as to adapt a base pre-trained model to low-resource languages.
Crowdsourcing Discourse Relation Annotations by a Two-Step Connective Insertion Task
The perspective of being able to crowd-source coherence relations bears the promise of acquiring annotations for new texts quickly, which could then increase the size and variety of discourse-annotated corpora.
How compatible are our discourse annotations? Insights from mapping RST-DT and PDTB annotations
Discourse-annotated corpora are an important resource for the community, but they are often annotated according to different frameworks.
Crowdsourcing discourse interpretations: On the influence of context and the reliability of a connective insertion task
In this paper, we investigate whether crowdsourcing can be used to obtain reliable discourse relation annotations.
Annotating Discourse Relations in Spoken Language: A Comparison of the PDTB and CCR Frameworks
In discourse relation annotation, there is currently a variety of different frameworks being used, and most of them have been developed and employed mostly on written data.
