---
title: "Classifier Ensembles for Dialect and Language Variety Identification — Dinu 2018"
description: "The paper tackles automatic identification of closely related language varieties in two VarDial 2018 shared tasks. 45 references, cited by 1 paper here."
authors: ["Liviu P. Dinu", "Alina Maria Ciobanu", "Marcos Zampieri", "Shervin Malmasi"]
year: 2018
venue: "arXiv:1808.04800"
source: "https://arxiv.org/abs/1808.04800"
doi: "10.48550/arxiv.1808.04800"
arxiv: "1808.04800"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/dinu-2018-classifier-ensembles-dialect-language-variety/"
---
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# Classifier Ensembles for Dialect and Language Variety Identification

**Authors:** [Liviu P. Dinu](https://md.research.elamur.ai/free/en/authors/liviu-p-dinu/), Alina Maria Ciobanu, [Marcos Zampieri](https://md.research.elamur.ai/free/en/authors/marcos-zampieri/) and [Shervin Malmasi](https://md.research.elamur.ai/free/en/authors/shervin-malmasi/) · **Year:** 2018 · **Venue:** arXiv:1808.04800 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1808.04800) · [PDF](https://arxiv.org/pdf/1808.04800) · [DOI 10.48550/arxiv.1808.04800](https://doi.org/10.48550/arxiv.1808.04800) · [arXiv 1808.04800](https://arxiv.org/abs/1808.04800) · [OpenAlex](https://openalex.org/W2887497299)

**Citations:** 3 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/dinu-2018-classifier-ensembles-dialect-language-variety/cited-by/) in our library, of the 140,884 papers whose bibliographies we read

## Summary
The paper tackles automatic identification of closely related language varieties in two VarDial 2018 shared tasks. The first is ADI, which covers four Arabic dialects plus Modern Standard Arabic in transcribed speech broadcasts. The second is DFS, which separates Dutch from Flemish in subtitle excerpts drawn from the SUBTIEL corpus. The authors reuse an approach they had applied to author profiling and native language identification. Each feature family gets its own linear SVM: TF-IDF character n-grams, word n-grams, and word k-skip bigrams. The individual predictions are then merged by an unweighted majority vote. Feature subsets and the regularization constant were chosen by grid search on development data. The ensembles beat the random baselines and their own strongest single member on the test sets. However, they finished in the lower part of both rankings, behind string-kernel and other systems. The work matters mainly as a carefully documented, easy-to-reproduce reference point for dialect and variety classification. It also shows candidly that a recipe that won earlier editions does not automatically transfer to new data.

## Key points
- **DFS data:** 320,500 SUBTIEL instances, about 11 million tokens in total. The split is 300,000 for training, 500 for development and 20,000 for testing.
- **ADI data and test design:** Participants received ASR output, acoustic features and phonetic features. The test set quietly mixed in-domain and out-of-domain material without telling participants.
- **Implementation:** Built with scikit-learn, using LinearSVC (Liblinear) members combined by VotingClassifier. Ties are resolved by the sorted order of the labels.
- **Selected configurations:**
  - DFS used character 3–6-grams plus word 3-grams with C = 100.
  - ADI used character 3–5-grams with C = 1.
- **Test results:**

| Task | Ensemble | Best single member | Random baseline | Rank | Winner |
|---|---|---|---|---|---|
| ADI | 0.500 macro F1 | 0.493 | 0.200 | last ranked | UnibucKernel, 0.589 |
| DFS | 0.596 macro F1 | 0.576 | 0.500 | 6th | Tübingen-Oslo, 0.660 |

## Interesting ideas
- **One learner per feature view.** Train a separate simple model on each feature view and let them vote, so every decision can be traced to which views agreed or disagreed.
- **Compare against the best member on both splits.** On development data the best single classifier slightly beat the selected ensemble, yet on test data the ensemble came out ahead, so picking a model by dev score alone can mislead.
- **Hide out-of-domain items in the test set.** Merging out-of-domain items into the test set without labeling them probes robustness rather than tuning to a known domain.
- **Break ties deterministically.** Resolving voting ties by a fixed label order keeps results reproducible across runs.
- **Diagnose with per-class confusion matrices.** The confusion matrices revealed which specific varieties got mixed up, such as Levantine with North African, and which was hardest, namely Gulf.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): The same pattern fits silver-data agreement, with each witness parser acting as a separate voter. Logging which witnesses formed the majority gives an explainable trust signal per arc.
- **I2** (learn): When evaluating gparse versus voting combinations, report both member and ensemble scores on held-out test data before discarding the ensemble.
- **I3** (answer): Fable QA and school-test benchmarks could secretly include texts from unfamiliar genres. This would check whether readers degrade gracefully.
- **I4** (tree): When multiple parsers or repair rules tie on a head or relation choice, a documented fixed tie-break rule keeps tree outputs stable and explainable.
- **I5** (tokens): A per-label confusion analysis of the tagger and morphology, for example between British and American spelling variants, would point to where targeted rules are needed.

*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/)

Topics: [🐪 Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/)

## Cite

Cite as: Liviu P. Dinu, Alina Maria Ciobanu, Marcos Zampieri and Shervin Malmasi. 2018. Classifier Ensembles for Dialect and Language Variety Identification. arXiv:1808.04800.

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

```bibtex
@misc{dinu2018classifier,
  title = {{Classifier Ensembles for Dialect and Language Variety Identification}},
  author = {Liviu P. Dinu, Alina Maria Ciobanu, Marcos Zampieri and Shervin Malmasi},
  year = {2018},
  doi = {10.48550/arxiv.1808.04800},
  eprint = {1808.04800},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1808.04800}
}
```

- [← previous Birzeit Arabic Dialect Identification System for the 2018 VarDial Challenge](https://md.research.elamur.ai/free/en/papers/naser-2018-birzeit-arabic-dialect-identification-system/)
- [next → Loss and preservation of case in Germanic non-standard varieties](https://md.research.elamur.ai/free/en/papers/baechler-2018-loss-preservation-case-germanic-non/)
- [↑ back to papers of 2018](https://md.research.elamur.ai/free/en/papers/2018/page/5/)

## References

45 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Wafia Adouane, Nasredine Semmar, and Richard Johansson. 2016. ASIREM Participation at the Discriminating Similar Languages Shared Task 2016. In Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3), pages 163–169, Osaka, Japan. [find](https://scholar.google.com/scholar?q=Wafia+Adouane%2C+Nasredine+Semmar%2C+and+Richard+Johansson%2E+2016%2E+ASIREM+Participation+at+the+Discriminating+Similar+Languages+Shared+Task+2016%2E+In+Proceedings+of+the+Third+Workshop+on+NLP+for+Similar+Lan)
2. Ahmed Ali, Najim Dehak, Patrick Cardinal, Sameer Khurana, Sree Harsha Yella, James Glass, Peter Bell, and Steve Renals. 2016. Automatic Dialect Detection in Arabic Broadcast Speech. In Proceedings of INTERSPEECH, pages 2934–2938. [find](https://scholar.google.com/scholar?q=Ahmed+Ali%2C+Najim+Dehak%2C+Patrick+Cardinal%2C+Sameer+Khurana%2C+Sree+Harsha+Yella%2C+James+Glass%2C+Peter+Bell%2C+and+Steve+Renals%2E+2016%2E+Automatic+Dialect+Detection+in+Arabic+Broadcast+Speech%2E+In+Proceedings+of+)
3. Ahmed Ali, Stephan Vogel, and Steve Renals. 2017. Speech Recognition Challenge in the Wild: Arabic MGB-3. arXiv preprint arXiv:1709.07276. [↗](https://arxiv.org/abs/1709.07276) [our card](https://md.research.elamur.ai/free/en/papers/ali-2017-speech-recognition-challenge-wild-arabic/)
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5. Adrien Barbaresi. 2018. Computationally Efficient Discrimination Between Language Varieties with Large Feature Vectors and Regularized Classifiers. In Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial). [find](https://scholar.google.com/scholar?q=Adrien+Barbaresi%2E+2018%2E+Computationally+Efficient+Discrimination+Between+Language+Varieties+with+Large+Feature+Vectors+and+Regularized+Classifiers%2E+In+Proceedings+of+the+Fifth+Workshop+on+NLP+for+Simi)
6. Andrei M. Butnaru and Radu Ionescu. 2018. UnibucKernel Reloaded: First Place in Arabic Dialect Identification for the Second Year in a Row. In Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial). [find](https://scholar.google.com/scholar?q=Andrei+M%2E+Butnaru+and+Radu+Ionescu%2E+2018%2E+UnibucKernel+Reloaded%3A+First+Place+in+Arabic+Dialect+Identification+for+the+Second+Year+in+a+Row%2E+In+Proceedings+of+the+Fifth+Workshop+on+NLP+for+Similar+Lang)
7. Çağrı Çöltekin, Taraka Rama, and Verena Blaschke. 2018. Tübingen-Oslo Team at the Var-Dial 2018 Evaluation Campaign: An Analysis of N-gram Features in Language Variety Identification. In Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial). [find](https://scholar.google.com/scholar?q=C%CC%A7ag%CC%86r%C4%B1+C%CC%A7o%CC%88ltekin%2C+Taraka+Rama%2C+and+Verena+Blaschke%2E+2018%2E+Tu%CC%88bingen%2DOslo+Team+at+the+Var%2DDial+2018+Evaluation+Campaign%3A+An+Analysis+of+N%2Dgram+Features+in+Language+Variety+Identification%2E+In+Proceedi)
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10. Alina Maria Ciobanu, Shervin Malmasi, and Liviu P. Dinu. 2018a. German Dialect Identification Using Classifier Ensembles. In Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial). [find](https://scholar.google.com/scholar?q=Alina+Maria+Ciobanu%2C+Shervin+Malmasi%2C+and+Liviu+P%2E+Dinu%2E+2018a%2E+German+Dialect+Identification+Using+Classifier+Ensembles%2E+In+Proceedings+of+the+Fifth+Workshop+on+NLP+for+Similar+Languages%2C+Varieties+a)
11. Alina Maria Ciobanu, Marcos Zampieri, Shervin Malmasi, Santanu Pal, and Liviu P. Dinu. 2018b. Discriminating between Indo-Aryan Languages Using SVM Ensembles. In Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial). [find](https://scholar.google.com/scholar?q=Alina+Maria+Ciobanu%2C+Marcos+Zampieri%2C+Shervin+Malmasi%2C+Santanu+Pal%2C+and+Liviu+P%2E+Dinu%2E+2018b%2E+Discriminating+between+Indo%2DAryan+Languages+Using+SVM+Ensembles%2E+In+Proceedings+of+the+Fifth+Workshop+on+N)
12. Steven Du and Yuan Yuan Wang. 2018. STEVENDU2018’s System in VarDial 2018: Discriminating between Dutch and Flemish in Subtitles. In Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial). [find](https://scholar.google.com/scholar?q=Steven+Du+and+Yuan+Yuan+Wang%2E+2018%2E+STEVENDU2018%E2%80%99s+System+in+VarDial+2018%3A+Discriminating+between+Dutch+and+Flemish+in+Subtitles%2E+In+Proceedings+of+the+Fifth+Workshop+on+NLP+for+Similar+Languages%2C+Var)
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16. Radu Tudor Ionescu and Andrei Butnaru. 2017. Learning to Identify Arabic and German Dialects using Multiple Kernels. In Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial), pages 200–209, Valencia, Spain, April. [find](https://scholar.google.com/scholar?q=Radu+Tudor+Ionescu+and+Andrei+Butnaru%2E+2017%2E+Learning+to+Identify+Arabic+and+German+Dialects+using+Multiple+Kernels%2E+In+Proceedings+of+the+Fourth+Workshop+on+NLP+for+Similar+Languages%2C+Varieties+and+D)
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