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# Contextualized Embeddings for Enriching Linguistic Analyses on Politeness

**Authors:** [Ahmad Aljanaideh](https://md.mova.elamur.ai/free/en/docs/papers/authors/ahmad-aljanaideh/), Eric Fosler-Lussier, [Marie-Catherine de Marneffe](https://md.mova.elamur.ai/free/en/docs/papers/authors/marie-catherine-de-marneffe/) · **Year:** 2020 · **Venue:** COLING 2020 **Link:** [aclanthology.org/2020.coling-main.198](https://aclanthology.org/2020.coling-main.198/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**2** citations](https://openalex.org/works?filter=cites:W3117445173) openalex · 2026-10-19[**6** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aljanaideh-2020-contextualized-embeddings-enriching-linguistic-analyses/) of the 140673 papers whose bibliographies we read[Proceedings of the 28th International Conference on Computational Linguistics](https://aclanthology.org/volumes/2020.coling-main/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2020.coling-main.198/)
- [PDF](https://aclanthology.org/2020.coling-main.198.pdf)
- [DOI 10.18653/v1/2020.coling-main.198](https://doi.org/10.18653/v1/2020.coling-main.198)
- [OpenAlex](https://openalex.org/W3117445173)

**Authors (3)**

1. [Ahmad Aljanaideh](https://openalex.org/A5010408246) [Semantic Scholar](https://www.semanticscholar.org/author/2373600281) · h 1 · 2 citations · 10 works
2. Eric Fosler-Lussier
3. [Marie-Catherine de Marneffe](https://openalex.org/A5112862689) [Semantic Scholar](https://www.semanticscholar.org/author/1738659017) · h 31 · 8698 citations · 167 works

Citations and author metrics from openalex (2026-10-19); authors with the same name are not always told apart.

## What it is about

Politeness studies count words such as *please*, *you* or hedges regardless of how they are used. Here every occurrence of a word in the Stanford Politeness Corpus (Wikipedia and Stack Exchange requests, polite/impolite quartiles) gets a contextual BERT vector, and the occurrences are split by a decision-tree-like procedure: pick two centroids, split by distance, keep the split with the best information gain on the polite/impolite labels, recurse to a depth set by the word's frequency. The clusters are readable. *Please* at the start of a request leans impolite, *please* in the middle leans polite, but *please* in the middle after a blunt opening («Would you please stop?») is impolite again; *can you* after a greeting is polite and after a challenging question impolite; *maybe* can invite («Maybe we can meet») or dismiss. Cluster ids used as features beat plain politeness-word features in a linear classifier, and the procedure rediscovers known markers such as repeated question marks.

## Ideas

- I1. Make Mova's politeness detectors in `world/src/talk.rs` interact with the opening of the request instead of adding up independently: the weight of *please*-in-the-middle or *can/could you* depends on whether the request opens with a greeting, thanks or first person (polite) or with a blunt second-person start or a challenge (impolite). Encode the few pairs the paper found as integer rules with the pair printed in the explanation; check on tale requests that the fired pairs read right.
- I2. Split a cue word's uses with Mova's own memory model rather than BERT: for each frequent cue in `prag/data/cues.tsv` (*maybe, please, think, just*), grow a small IGTree on the existing context slots (position, opening word, form, `you`, `neg`) with the silver labels (`hedge`, `polarity`, `act`) and keep only branches that are pure and well supported. The tree is the readable analysis the paper gets from clustering. Measure hedge and polarity macro-F1 with the split cues against the plain ones.
- I3. Add *please* position (initial, medial, final) as a feature value instead of the present yes/no `please` slot in `prag/src/feats.rs`; the start/middle contrast is one of the strongest politeness facts. Measure request and polarity scores.

## For Mova

[32. Politeness and hedging](https://md.mova.elamur.ai/free/en/docs/languages/en/32-politeness-and-hedging/) §2 (politeness in English grammar), §6 (computational politeness, Mova's detectors), §7 (hedging). Code: `world/src/talk.rs`, `prag/src/feats.rs`, `prag/data/cues.tsv`, `prag/src/model.rs` (IGTree). Measures: hedge/polarity/request macro-F1 in `prag eval`; checked examples of fired politeness pairs.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2020.coling-main.198](https://doi.org/10.18653/v1/2020.coling-main.198)

```
@inproceedings{aljanaideh2020contextualized,
  title = {{Contextualized Embeddings for Enriching Linguistic Analyses on Politeness}},
  author = {Aljanaideh, Ahmad and Fosler-Lussier, Eric and Marneffe, Marie-Catherine de},
  booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
  year = {2020},
  doi = {10.18653/v1/2020.coling-main.198},
  url = {https://doi.org/10.18653/v1/2020.coling-main.198}
}
```

- [← previous Modeling English Past Tense Intuitions with Minimal Generalization](https://md.mova.elamur.ai/free/en/docs/papers/albright-2002-modeling-english-past-tense-intuitions/)
- [next → Speech Act Patterns for Improving Generalizability of Explainable Politeness Detection Models](https://md.mova.elamur.ai/free/en/docs/papers/aljanaideh-2025-speech-act-patterns-improving-generalizability/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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