Papers › Discourse Coherence in the Wild: A Dataset, Evaluation and Methods
Discourse Coherence in the Wild: A Dataset, Evaluation and Methods
Alice Lai, Joel Tetreault
To date there has been very little work on assessing discourse coherence methods on real-world data. To address this, we present a new corpus of real-world texts (GCDC) as well as the first large-scale evaluation of leading discourse coherence algorithms. We show that neural models, including two that we introduce here (SentAvg and ParSeq), tend to perform best. We analyze these performance differences and discuss patterns we observed in low coherence texts in four domains.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Coherence Evaluation | GCDC + RST - Accuracy | ParSeq | Accuracy | 55.09 | #3 of 4 | Archive leaderboard | report |
| Coherence Evaluation | GCDC + RST - F1 | ParSeq | Average F1 | 46.65 | #2 of 3 | Archive leaderboard | report |
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