Examples and Specifications that Prove a Point: Identifying Elaborative and Argumentative Discourse Relations
Summary
Examples and specifications are among the most frequent discourse relations, but annotated corpora often disagree on how to label them. Using the WSJ-Aligned corpus, which maps Penn Discourse Treebank (PDTB) and RST-DT annotations of the same Wall Street Journal texts onto each other, Scholman and Demberg show that PDTB's INSTANTIATION and SPECIFICATION relations are often labelled as argumentative (causal) in RST-DT. The reverse pattern is much rarer.
To test how ordinary readers actually understand these relations, they ran a crowdsourcing study. Untrained participants inserted connectives, chosen from a small and fairly unambiguous predefined list, between the two segments. A replication without surrounding context gave highly similar results.
Readers frequently interpret these relations as argumentative. Examples and specifications can therefore illustrate a situation and support a claim at the same time. The authors argue that annotation schemes should capture such multiple simultaneous readings. They also see connective-insertion crowdsourcing as a cheap and replicable way to obtain distributions of relation senses, and potentially to build a corpus.
Key points
- Agreement between frameworks: PDTB and RST-DT agree on about 60% of explicit relations but only about 35% of implicit ones. Agreement is just 32% for INSTANTIATION and 14% for SPECIFICATION.
- Frequency: INSTANTIATION and SPECIFICATION together make up 24% of implicit relations in the PDTB.
- Asymmetry between frameworks:
- Among 306 PDTB INSTANTIATION relations, 25% receive causal labels (EXPLANATION-ARGUMENTATIVE, EVIDENCE) in RST-DT.
- Among 426 PDTB SPECIFICATION relations, the figure is 21%.
- In the other direction, only 7% of RST EXAMPLE and 12% of RST GENERAL-SPECIFIC relations get causal labels in the PDTB.
- Use of the second connective: Participants could insert a second connective but rarely did. The authors suggest making a second choice mandatory, with a "no other connective fits" option.
- Domain knowledge: Items with highly scattered answers likely reflect participants' limited knowledge of economics, which the authors note could also affect trained annotators.
Cited by
- Exploring the Potential of ChatGPT on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations
- Is there less annotator agreement when the discourse relation is underspecified?
- Extending Implicit Discourse Relation Recognition to the PDTB-3
- Multi-Label Classification for Implicit Discourse Relation Recognition
- DiscoGeM: A Crowdsourced Corpus of Genre-Mixed Implicit Discourse Relations
- Crowdsourcing Discourse Relation Annotations by a Two-Step Connective Insertion Task
Cite
@misc{scholman2017examples,
title = {{Examples and Specifications that Prove a Point: Identifying Elaborative and Argumentative Discourse Relations}},
author = {Scholman, Merel C.J. and Demberg, Vera},
year = {2017},
doi = {10.5087/dad.2017.203},
url = {https://doi.org/10.5087/dad.2017.203}
}References
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