---
title: "Can You Be More Explicit? A Task and Dataset — Zeidler 2026"
description: "The paper looks at explicitations : edits that spell out meaning a reader could probably have inferred anyway, such as adding a reason for an instruction."
authors: ["Laura Zeidler", "Michael Roth"]
year: 2026
venue: "Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*SEM 2026)"
source: "https://aclanthology.org/2026.starsem-conference.12/"
doi: "10.18653/v1/2026.starsem-conference.12"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zeidler-2026-can-you-be-more-explicit/"
---
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# Can You Be More Explicit? A Task and Dataset on Explicitations of Implicit Meaning

**Authors:** Laura Zeidler, [Michael Roth](https://md.research.elamur.ai/free/en/authors/michael-roth/) · **Year:** 2026 · **Venue:** Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (\*SEM 2026) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.starsem-conference.12/) · [PDF](https://aclanthology.org/2026.starsem-conference.12.pdf) · [DOI 10.18653/v1/2026.starsem-conference.12](https://doi.org/10.18653/v1/2026.starsem-conference.12) · [OpenAlex](https://openalex.org/W7166844406)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary
The paper looks at *explicitations*: edits that spell out meaning a reader could probably have inferred anyway, such as adding a reason for an instruction. The goal is to tell these apart from edits that add genuinely new content, as groundwork for making instructional texts easier to understand.

The authors mine wikiHowToImprove revision histories with rules that pick out short multi-word insertions, plus filters that remove likely new-information cases (numbers, named entities, lists). Crowdworkers on Prolific labelled each candidate as Implicit or New Info, and the labels were aggregated with MACE.

The resulting judgments are often subjective and depend on readers' background knowledge. Even so, linguistic analyses show systematic differences between the two classes. Off-the-shelf LLMs perform well, but few-shot prompting and fine-tuning help them inconsistently, and fine-tuning helps only in-domain. Fine-tuned NLI models, by contrast, improve reliably with supervision and hold up better on unseen topics. This suggests the released dataset carries learnable signal for future work on enriching procedural texts.

## Key points
- Candidates come from wikiHowToImprove (about 2.7 million aligned revisions). The extra filters raised the share of Implicit items in the pilot from 57% to 65%.
- Each item received 8 crowd annotations. Annotators with a MACE score below 0.5 were removed, which raised Krippendorff's α from 0.301 to 0.462 in the main phase.
- The main phase covers 1,632 revisions from 18 wikiHow topics; Holidays and Tradition was dropped because of unreliable annotators. The training split has 1,150 items (80%).
- Business and Hobbies are held out as an out-of-domain test set, on which NLI models proved more robust than LLMs.
- Five LLMs were tested. Data and code are released at github.com/lurr98/classifying_implicit_meaning; the dataset is English-only.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zeidler2026can,
  title = {{Can You Be More Explicit? A Task and Dataset on Explicitations of Implicit Meaning}},
  author = {Zeidler, Laura and Roth, Michael},
  booktitle = {Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*SEM 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.starsem-conference.12},
  url = {https://doi.org/10.18653/v1/2026.starsem-conference.12}
}
```

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