The Impact of Deep Hierarchical Discourse Structures in the Evaluation of Text Coherence
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Bibliographic record
Abstract
Previous work by Lin et al. (2011) demonstrated the effectiveness of using discourse relations for evaluating text coherence. However, their work was based on discourse relations annotated in accordance with the Penn Discourse Treebank (PDTB) (Prasad et al., 2008), which encodes only very shallow discourse structures; therefore, they cannot capture long-distance discourse dependencies. In this paper, we study the impact of deep discourse structures for the task of co-herence evaluation, using two approaches: (1) We compare a model with features derived from discourse relations in the style of Rhetorical Structure Theory (RST) (Mann and Thompson, 1988), which annotate the full hierarchical discourse structure, against our re-implementation of Lin et al.’s model; (2) We compare a model encoded using only shallow RST-style discourse relations, against the one encoded using the complete set of RST-style discourse relations. With an evaluation on two tasks, we show that deep discourse structures are truly useful for better dif-ferentiation of text coherence, and in general, RST-style encoding is more powerful than PDTB-style encoding in these settings. 1
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it