---
title: "FL-MSCL — paper by Shijia et al. (2026) — summary and ideas"
description: "The authors build a unified training corpus by merging existing English metaphor, metonymy and simile benchmarks… 86.1 F1. 20 references."
authors: ["Lu Shijia", "Fumiyo Fukumoto", "Huang Xiaoxi", "Yoshimi Suzuki"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)"
source: "https://aclanthology.org/2026.acl-short.57/"
doi: "10.18653/v1/2026.acl-short.57"
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/shijia-2026-fl-mscl-unified-figurative-language/"
---
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# FL-MSCL: A Unified Figurative Language Detection Model Driven by Multi-Type Signals and Contrastive Learning

**Authors:** Lu Shijia, Fumiyo Fukumoto, Huang Xiaoxi and Yoshimi Suzuki · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-short.57/) · [PDF](https://aclanthology.org/2026.acl-short.57.pdf) · [DOI 10.18653/v1/2026.acl-short.57](https://doi.org/10.18653/v1/2026.acl-short.57) · [OpenAlex](https://openalex.org/W7166862858)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Figurative language detection is usually handled one figure at a time, as a binary choice between figurative and literal. That leaves a gap: metaphor, metonymy and simile are easy to confuse with one another. This short ACL 2026 paper treats them together as a single sentence-level task with four labels: literal, metaphor, metonymy and simile. The authors build a unified training corpus by merging existing English metaphor, metonymy and simile benchmarks and down-sampling the result into balanced classes. Their model, FL-MSCL, uses a DeBERTa encoder. For each input sentence, the prompt is extended with one retrieved example per category, chosen by similarity using Sentence-BERT and FAISS. Training combines cross-entropy with a supervised contrastive loss on the [CLS] representation, which separates the confusable categories more sharply. On the unified task the full model reaches 86.1 macro-F1. It also performs strongly on standard single-figure benchmarks, including a reported new best on MOH-X. The work suggests that explicitly modelling the boundaries between related figures helps both joint and category-specific detection, though all experiments are English-only.

## Key points
- **Setup:** DeBERTa-v3-base encoder. Retrieved exemplars act as "semantic anchors" in place of the static dictionary definitions used in earlier knowledge-injection work. The total loss is cross-entropy plus λ·SCL.
- **Data:** VUA-18, VUA-20, VUA-Verb, MOH-X and TroFi (metaphor), ReLocaR (metonymy) and PLMs-Simile (simile). Binary evaluations follow the official splits.
- **Unified 4-way task:** 86.1 F1 with precision 88.8 and recall 84.1. Contrastive learning alone mainly raises precision, while adding prompts restores recall.
- **Metaphor:** 86.2 F1 on MOH-X (claimed new SOTA) and 88.9 F1 on VUA-Verb, reported as 14.8 points above WPDM. Without both prompts and contrastive loss, VUA-Verb drops to 81.9.
- **Other figures and errors:** ReLocaR reaches 89.5 F1 versus 84.8 for PreWin, and simile detection reaches 99.9 F1. Remaining weaknesses are cautious metaphor recall on VUA-18, institutional metonymy such as "Washington said", and similes without explicit markers.

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

## Institutions

Also on the byline: [Meiji University](https://ror.org/02rqvrp93) · [University of Yamanashi](https://ror.org/059x21724).

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## Cite

Cite as: Lu Shijia, Fumiyo Fukumoto, Huang Xiaoxi and Yoshimi Suzuki. 2026. FL-MSCL: A Unified Figurative Language Detection Model Driven by Multi-Type Signals and Contrastive Learning. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers).

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

```bibtex
@inproceedings{shijia2026fl,
  title = {{FL-MSCL: A Unified Figurative Language Detection Model Driven by Multi-Type Signals and Contrastive Learning}},
  author = {Lu Shijia, Fumiyo Fukumoto, Huang Xiaoxi and Yoshimi Suzuki},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-short.57},
  url = {https://doi.org/10.18653/v1/2026.acl-short.57}
}
```

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## References

20 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*