---
title: "SemEval-2022 Task 11 — Malmasi 2022"
description: "This overview reports on the SemEval-2022 shared task MultiCoNER, which targets named entities that are hard to spot: titles of creative works, product…"
authors: ["Shervin Malmasi", "Anjie Fang", "Besnik Fetahu", "Sudipta Kar", "Oleg Rokhlenko"]
year: 2022
venue: "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)"
source: "https://aclanthology.org/2022.semeval-1.196/"
doi: "10.18653/v1/2022.semeval-1.196"
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/malmasi-2022-semeval-2022-task-11-multilingual/"
---
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# SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER)

**Authors:** [Shervin Malmasi](https://md.research.elamur.ai/free/en/authors/shervin-malmasi/), [Anjie Fang](https://md.research.elamur.ai/free/en/authors/anjie-fang/), [Besnik Fetahu](https://md.research.elamur.ai/free/en/authors/besnik-fetahu/), [Sudipta Kar](https://md.research.elamur.ai/free/en/authors/sudipta-kar/), [Oleg Rokhlenko](https://md.research.elamur.ai/free/en/authors/oleg-rokhlenko/) · **Institutions:** [Amazon](https://md.research.elamur.ai/free/en/companies/amazon/) · **Year:** 2022 · **Venue:** Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.semeval-1.196/) · [PDF](https://aclanthology.org/2022.semeval-1.196.pdf) · [DOI 10.18653/v1/2022.semeval-1.196](https://doi.org/10.18653/v1/2022.semeval-1.196) · [OpenAlex](https://openalex.org/W4287854446)

**Citations:** 104 citations (OpenAlex, 2026-10-14) · [86](https://md.research.elamur.ai/free/en/papers/malmasi-2022-semeval-2022-task-11-multilingual/cited-by/) in our library, of the 140884 papers whose bibliographies we read

## Summary
This overview reports on the SemEval-2022 shared task MultiCoNER, which targets named entities that are hard to spot: titles of creative works, product names and groups. These entities are often ambiguous, look like ordinary phrases, appear in short uncased inputs, and keep emerging over time. The organizers built the MultiCoNER dataset, about 2.3 million instances in 11 languages, drawn from three domains: Wikipedia sentences (LOWNER), questions (MSQ-NER) and search queries (ORCAS-NER). It comes with monolingual, multilingual and code-mixed subsets, for 13 tracks in total. Systems were ranked by macro-averaged F1. Fifty-five teams took part in the test phase. The strongest systems combined Transformer encoders (mostly XLM-RoBERTa) with external knowledge, such as retrieved Wikipedia text or gazetteers, and used model ensembles. Winners beat the baseline by roughly 35 F1 points on average. Even so, creative works and groups remained the weakest classes. The task matters because standard benchmarks like CoNLL03 overstate NER quality, and MultiCoNER offers a multilingual testbed for realistic, open-world entity recognition.

## Key points
- Languages: the 11 covered languages are English, German, Dutch, Spanish, Russian, Turkish, Farsi, Hindi, Bangla, Korean and Chinese. Tags follow the six-class WNUT 2017 taxonomy: PER, LOC, CORP, GRP, PROD, CW.
- Each language has 15,300 training and 800 development sentences. Test sets are far larger, up to about 218k sentences per language. The MULTI test set has 471,911 sentences, and MIX has 100k.
- Training data is almost entirely Wikipedia. Only 100 sentences per track represent the other domains (50 questions and 50 queries), which makes domain transfer part of the challenge.
- Automatic labels were derived from Wikipedia links and Wikidata. A manual check of 400 English sentences found 94% label accuracy.
- Participation: 377 participants in the practice phase and 236 in the test phase. There were 1,884 test submissions from 55 teams and 34 system papers.
- Top teams:
  - DAMO-NLP retrieved Wikipedia paragraphs as extra context and led most tracks.
  - USTC-NELSLIP fused gazetteer knowledge into the model.
- Teams that only fine-tuned a pre-trained model scored close to the baseline. CRF layers and ensembles were common among strong systems.

## Interesting ideas
- **Small train set, huge test set.** Pairing a small training set with a very large test set full of unseen entities measures generalization rather than memorization. This design could be reused for evaluating Ukrainian NER or any task with long-tail vocabularies.
- **Template-based data for new domains.** Questions and queries were turned into templates by masking their entities. The templates were translated, then filled with entity names from the target language. This is a cheap way to create labeled data for short-text domains in new languages.
- **Synthetic code-mixed test data.** The code-mixed set was made by replacing entity surface forms with their equivalents in another language. This tests cross-lingual robustness without collecting naturally mixed text.
- **Retrieval for domain transfer.** Adding retrieved encyclopedic context at training and inference time helped on the main domain and also on unseen question and query domains. This suggests retrieval augmentation as a general fix when target-domain data is scarce.
- **Per-class and per-domain reporting.** Breaking scores down by class and by domain, alongside macro F1, showed exactly where external knowledge helps (CW, GRP). That kind of breakdown is more informative than a single aggregate score.

*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: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Named entity recognition](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/) · [Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/)

## Cite

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

```bibtex
@inproceedings{malmasi2022semeval,
  title = {{SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER)}},
  author = {Malmasi, Shervin and Fang, Anjie and Fetahu, Besnik and Kar, Sudipta and Rokhlenko, Oleg},
  booktitle = {Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)},
  year = {2022},
  doi = {10.18653/v1/2022.semeval-1.196},
  url = {https://doi.org/10.18653/v1/2022.semeval-1.196}
}
```

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## References

75 references, 16 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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