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Found 21 papers, 1 papers with code
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).
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.
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.
Establishing Annotation Quality in Multi-label Annotations
In many linguistic fields requiring annotated data, multiple interpretations of a single item are possible.
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
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
Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Design
Disagreement in natural language annotation has mostly been studied from a perspective of biases introduced by the annotators and the annotation frameworks.
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.
Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification
Implicit discourse relation classification is one of the most challenging and important tasks in discourse parsing, due to the lack of connective as strong linguistic cues.
Do Speakers Produce Discourse Connectives Rationally?
A number of different discourse connectives can be used to mark the same discourse relation, but it is unclear what factors affect connective choice.
Can Discourse Relations be Identified Incrementally?
Humans process language word by word and construct partial linguistic structures on the fly before the end of the sentence is perceived.
Using Explicit Discourse Connectives in Translation for Implicit Discourse Relation Classification
Implicit discourse relation recognition is an extremely challenging task due to the lack of indicative connectives.
General Classification
Implicit Discourse Relation Classification
+4
