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# "You might think about slightly revising the title": Identifying Hedges in Peer-tutoring Interactions

**Authors:** Yann Raphalen, [Chloé Clavel](https://md.mova.elamur.ai/free/en/docs/papers/authors/chloe-clavel/), [Justine Cassell](https://md.mova.elamur.ai/free/en/docs/papers/authors/justine-cassell/) · **Year:** 2022 · **Venue:** ACL 2022 (long papers) **Link:** [aclanthology.org/2022.acl-long.153](https://aclanthology.org/2022.acl-long.153/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**10** citations](https://openalex.org/works?filter=cites:W4285203730) openalex · 2026-10-18[**5** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/raphalen-2022-you-might-think-about-slightly/) of the 140673 papers whose bibliographies we read[Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://aclanthology.org/volumes/2022.acl-long/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2022.acl-long.153/)
- [PDF](https://aclanthology.org/2022.acl-long.153.pdf)
- [DOI 10.18653/v1/2022.acl-long.153](https://doi.org/10.18653/v1/2022.acl-long.153)
- [arXiv 2306.14911](https://arxiv.org/abs/2306.14911)
- [OpenAlex](https://openalex.org/W4285203730)

**Authors (3)**

1. [Yann Raphalen](https://openalex.org/A5035583416) h 1 · 10 citations · 3 works
2. [Chloé Clavel](https://openalex.org/A5064876801) [ORCID](https://orcid.org/0000-0003-4850-3398) · h 23 · 2109 citations · 255 works
3. [Justine Cassell](https://openalex.org/A5109964078) [ORCID](https://orcid.org/0000-0003-2770-7359) · h 57 · 13275 citations · 314 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The paper classifies clauses of teenage peer-tutoring dialogue (about 24,000 clauses, more than 90% not hedged; data under NDA) into three hedge types — subjectivizers («I think», «I would»), propositional hedges or approximators («just», «kind of», «or whatever») and apologizers used as softeners («oh sorry, that's nine») — plus «not hedged». The authors first fix inconsistent annotation of *just* and *would*, then compare a regular-expression rule classifier built from Brown & Levinson, Fraser and Rowland, gradient-boosted trees over interpretable features (the rule label weighted by the rule's precision, lemma uni/bigrams, POS n-grams, LIWC, tutoring moves, nonverbal cues), MLP/LSTM with sentence embeddings, an attention CNN and fine-tuned BERT. Trees over the rule label plus n-grams win (weighted F1 79.0 on the three hedge classes vs 70.6 for BERT and 67.6 for the rules alone); adding POS and LIWC barely helps, nonverbal and tutoring-move features not at all. SHAP shows the top features per class: the rule label, «I», «would», «just», «sorry», and «oh» before «sorry».

## Ideas

- I1. Split Mova's binary `hedge` into the three functional classes (subjectivizer / approximator / softening apology) in `prag/src/schema.rs`. Chapter 32 already notes that the label has no shield–approximator distinction. The cue classes in `prag/data/cues.tsv` map almost directly: *I think, I guess* → shield; *kind of, sort of, a bit, almost* → approximator; *sorry* inside a clause that carries a proposition → softener.
- I2. Use the paper's hybrid recipe in the SLM: feed the rule-based hedge decision, weighted by that rule's measured precision on the silver, as one slot into the perceptron and IGTree (`prag/src/feats.rs`), next to the raw word slots. Measure hedge macro-F1 in `prag eval` with and without the slot.
- I3. A standalone «Oh sorry» with no proposition is not a hedge (it is an apology or repair); only a «sorry» attached to content softens it. Encode this as a rule for `act=apologize` versus `hedge=1`, and check the silver for both cases.
- I4. *would* and *just* are the hardest cues; the rule classifier lost precision when it added *would*. Before adding them to `cues.tsv`, measure their per-cue precision against the silver `hedge` label.

## For Mova

[32. Politeness and hedging](https://md.mova.elamur.ai/free/en/docs/languages/en/32-politeness-and-hedging/) §7 (approximators vs shields) and §9–10; `prag/src/schema.rs` (hedge type), `prag/src/feats.rs`, `prag/data/cues.tsv`; measure: hedge accuracy and macro-F1 in `prag eval`, with the shuffled-label control.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2022.acl-long.153](https://doi.org/10.18653/v1/2022.acl-long.153) · [arXiv 2306.14911](https://arxiv.org/abs/2306.14911)

```
@inproceedings{raphalen2022you,
  title = {{"You might think about slightly revising the title": Identifying Hedges in Peer-tutoring Interactions}},
  author = {Raphalen, Yann and Clavel, Chloé and Cassell, Justine},
  booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2022},
  doi = {10.18653/v1/2022.acl-long.153},
  eprint = {2306.14911},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2022.acl-long.153}
}
```

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