The Impact of Deep Hierarchical Discourse Structures in the Evaluation of Text Coherence
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".