---
title: "TAMA — paper by Liu et al. (2026) — summary and ideas"
description: "Most moderation systems label a post as abusive or not and ignore who is being attacked. 32 references."
authors: ["JiYan Liu", "Youzheng Liu", "Taihang Wang", "Yimin Wang", "Ye Jiang", "Diana Maynard"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.811/"
doi: "10.18653/v1/2026.acl-long.811"
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/liu-2026-tama-target-aware-multilingual-abuse/"
---
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# TAMA: Target-Aware Multilingual Abuse Detection by Cascaded Conditional Multi-Task Learning

**Authors:** JiYan Liu, Youzheng Liu, Taihang Wang, Yimin Wang, Ye Jiang and [Diana Maynard](https://md.research.elamur.ai/free/en/authors/diana-maynard/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long 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-long.811/) · [PDF](https://aclanthology.org/2026.acl-long.811.pdf) · [DOI 10.18653/v1/2026.acl-long.811](https://doi.org/10.18653/v1/2026.acl-long.811) · [OpenAlex](https://openalex.org/W7166877489)

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

## Summary
Most moderation systems label a post as abusive or not and ignore who is being attacked. This makes it hard to separate legitimate criticism of public figures from harassment aimed at a specific person. The authors address this with TAMA, a multilingual benchmark of 9,386 X (Twitter) posts addressed to five politicians and journalists. Each post is paired with a designated target and carries three linked annotations:
- whether the abuse targets that person, someone else, or nobody;
- one of 12 fine-grained abuse types;
- character-level spans marking the abusive phrases.

They also propose Cascaded-MTL, a multi-task model with a shared encoder. Its predictions flow in order from target, to type, to span through three small conditioning modules: CTF, TAG and LGSD. Across three multilingual encoders, the full cascade gives the best average F1 compared with single-task and plain multi-task training. The largest gains are in span localization. The work supports explainable, person-specific moderation that protects public figures, and both code and data are publicly released.

## Key points
- **Data collection:** TAMA was built from a pool of 218,259 original posts directed at five people: Vladimir Soloviev, Yulia Navalnaya, Maria Ressa, Liz Truss and Theresa May. The posts cover English, Russian, Filipino, Spanish and Italian, among other languages.
- **Tasks:** T1 is a three-way target label (non-abusive / targeted-abusive / unidentified-targets). T2 uses the 12-category taxonomy of Posetti et al. (2023), ranging from death_threat to general. T3 is abusive span extraction using character offsets.
- **Conditioning modules:** CTF merges target logits into the features used for type classification. TAG gates the type representation using target probabilities. LGSD makes span tagging depend on the predicted type distribution.
- **Span results:** Compared with MTL-Base, conditioning raises T3_F1 on all three backbones:
  - multilingual-e5-base: 72.19 → 77.97
  - mDeBERTa-v3-base: 70.93 → 76.40
  - XLM-RoBERTa-base: 70.62 → 76.24
- **Average F1 and limitations:** For XLM-RoBERTa-base, average F1 rises from 60.00 (single-task) to 65.09 (full cascade). The authors note several limitations: only five targets, a long-tailed distribution of abuse types, and errors in upstream predictions that can carry into later tasks.

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

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🧭 Topics (1): [🌍 Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · 828 works

## Cite

Cite as: JiYan Liu, Youzheng Liu, Taihang Wang, Yimin Wang, Ye Jiang and Diana Maynard. 2026. TAMA: Target-Aware Multilingual Abuse Detection by Cascaded Conditional Multi-Task Learning. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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

```bibtex
@inproceedings{liu2026tama,
  title = {{TAMA: Target-Aware Multilingual Abuse Detection by Cascaded Conditional Multi-Task Learning}},
  author = {JiYan Liu, Youzheng Liu, Taihang Wang, Yimin Wang, Ye Jiang and Diana Maynard},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.811},
  url = {https://doi.org/10.18653/v1/2026.acl-long.811}
}
```

- [← previous TalkTag: Fine-Grained Morphosyntactic Error Annotation for Transcribed Speech](https://md.research.elamur.ai/free/en/papers/venturini-2026-talktag-fine-grained-morphosyntactic-error/)
- [next → TamilTok: Morphologically-Informed Tokenization for Tamil](https://md.research.elamur.ai/free/en/papers/muthukumar-2026-tamiltok-morphologically-informed-tokenization-tamil/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/44/)

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*Updated 1 November 2026*