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SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets

Summary

Code-mixing, where people alternate between languages within one message, is common on social media. It breaks NLP tools built on monolingual resources, especially when one language is written phonetically in another script or when words get inflected by the other language's grammar. To push research on this, the SentiMix shared task at SemEval-2020 asked systems to label code-mixed tweets as positive, negative, or neutral. The organizers released two corpora: Hinglish (Hindi-English, with Hindi typed in Latin script) and Spanglish (Spanish-English). Every tweet has a sentence-level sentiment label, and every token has a language tag. Submissions were run through CodaLab and ranked by weighted F1 on a hidden test set. The task drew 89 final submissions and 33 system papers. The top systems reached 75.0% F1 on Hinglish and 80.6% on Spanglish. Most strong entries used BERT-style pretrained models or ensembles. The work matters because annotated code-mixed data remains rare, and these corpora can also support other tasks such as language identification.

Key points

  • Corpus sizes:
    • Hinglish has 20,000 tweets (14,000 train, 3,000 validation, 3,000 test) with roughly balanced classes.
    • Spanglish has 18,789 tweets (12,002 train, 2,998 validation, 3,789 test).
  • Spanglish label shift: the Spanglish test set is heavily skewed. About 81% of test tweets are positive, compared with about 50% in train.
  • Spanglish annotation source: sentiment was annotated with the SentiStrength scheme on top of the combined corpus from the three CALCS workshops.
  • Participation: 61 submissions for Hinglish and 28 for Spanglish.
  • Evaluation:
    • The ranking metric was weighted F1 across the three classes, with per-class precision and recall also reported.
    • Only each team's first three test submissions counted, and the best of those was used for ranking.
  • Methods and resources:
    • Participant approaches ranged from NB, LR, RF, and SVM through CNN and RNN/LSTM/GRU to BERT, XLNet, XLmR, and ALBERT.
    • Transformer models gave the best results.
    • Ensembles always beat their individual component models.
    • The data is publicly available on Zenodo.

Interesting ideas

  • I1. Layered annotation: adding token-level language tags next to the sentence-level sentiment label makes one dataset usable for several tasks. This is cheap extra value for any mixed-language corpus.
  • I2. Leaderboard hygiene: capping the number of test submissions and scoring only the best of the first few limits overfitting to the test set. Teams were also asked to declare any external resources, which avoids a rigid split into constrained and unconstrained tracks while keeping results interpretable.
  • I3. Reporting beyond one score: weighted F1 handles imbalanced classes, and per-class precision and recall show where errors come from. The skewed Spanglish test set illustrates why this kind of breakdown matters.
  • I4. Measuring mixing intensity: the Code Mixing Index cited in the paper scores how mixed a sentence is from the counts of each language's tokens and the number of language switches. It could be used to stratify data or analyze errors by degree of mixing.
  • I5. Techniques for scarce or noisy data: the organizers single out two participant ideas for low-resource, spelling-variable text.
    • Pseudo-labeling and cross-lingual embeddings can stretch small annotated datasets.
    • Morpheme- or phoneme-based subword units may cope better with heavy spelling variation than standard subwords.

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

Cited by

90 papers of our library cite it (of the 140884 whose bibliographies we read); 2 of them have a page here, 88 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2020.semeval-1.100

@inproceedings{patwa2020semeval,
  title = {{SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets}},
  author = {Patwa, Parth and Aguilar, Gustavo and Kar, Sudipta and Pandey, Suraj and PYKL, Srinivas and Gambäck, Björn and Chakraborty, Tanmoy and Solorio, Thamar and Das, Amitava},
  booktitle = {Proceedings of the Fourteenth Workshop on Semantic Evaluation},
  year = {2020},
  doi = {10.18653/v1/2020.semeval-1.100},
  url = {https://doi.org/10.18653/v1/2020.semeval-1.100}
}

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