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QCAW 1.0: Building a Qatari Corpus of Student Argumentative Writing

Paper · 2024 · Wajdi Zaghouani, Abdelhamid Ahmed, Xiao Zhang et al.

Summary

The paper describes QCAW, the Qatari Corpus of Argumentative Writing. It addresses a gap in learner-corpus resources: almost no bilingual data exist in which the same people write in their native Arabic and in English as a second language. The authors collected argumentative essays from 195 Qatari university students, all native Arabic speakers proficient in English. Each student wrote one essay in Arabic and one in English, on two different topics. This yields roughly 200,000 tokens split evenly between the two languages. The essays come with part-of-speech annotated files and detailed metadata about writers and writing conditions. Essays were also rated for overall quality and for voice, and the corpus was examined statistically. The authors present QCAW as the first publicly available collection of paired L1 Arabic and L2 English essays. Because the writer is held constant across languages, the corpus supports research on contrastive rhetoric, error analysis, automated writing evaluation and writing pedagogy for Arab learners.

Key points

  • The corpus has 390 essays by 195 students (159 female, 36 male): 195 Arabic essays (97,248 tokens) and 195 English essays (98,379 tokens).
  • Writer metadata covers gender, major, and first and second language. Essay metadata covers serial number, word limit, genre, writing date, time spent and location.
  • Both language sections come with part-of-speech annotated versions, and the essays carry ratings for overall writing quality and voice.
  • QCAW was released through the Linguistic Data Consortium (Ahmed et al., 2022).
  • The authors compare it with ZAEBUC, a mostly Emirati bilingual corpus whose English side holds 388 essays totalling 88,000 words and whose Arabic side holds 214 essays totalling 33,000 words. ZAEBUC is less balanced across the two languages.

Interesting ideas

  • I1. Pairing texts by author rather than by translation keeps the person constant while the language changes, so differences can be attributed to language rather than to the individual.
  • I2. The corpus records the conditions of writing (time spent, word limit, location, date) next to each text, which lets results be broken down by circumstance instead of only averaged.
  • I3. Authentic L2 learner writing contains the grammatical, lexical and orthographic errors typical of non-native writers, which makes it a realistic stress test rather than clean edited prose.
  • I4. Rating each essay on separate dimensions (overall quality vs. voice) instead of one global score supports multifaceted analysis of the same text.
  • I5. Keeping genre fixed (argumentative only) while varying topic makes the collection homogeneous enough for contrastive comparisons.

For Mova

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

  • I1 (learn): when evaluating, we could build matched pairs of texts with one shared factor (same story, different wording) as a control, to check whether a score difference comes from the factor we intend to measure.
  • I2 (learn): attaching provenance and condition fields to every silver or test sentence would let us slice parser and reader accuracy by source and genre and spot where errors cluster.
  • I3 (tree): parsing learner English essays could reveal how the perceptron parser and gparse degrade on non-native article use, tense overgeneralisation and run-on syntax, and could suggest repair rules.
  • I4 (back): when judging generated or round-tripped text from the text → graph → text compressor, we could score fidelity and fluency/style as separate axes so that one does not mask the other.
  • I5 (frames): a genre-controlled argumentative subset would be a clean testbed for detecting claim, reason and concession speech acts without genre confounds.

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, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksPsychology · 1,552 worksDevelopmental and Educational Psychology · 499 worksLiterature and Literary Theory · 519 works

🧭 Topics 2Research subjects and languages the work is about.Semitic languages · 2,908 worksGrammatical error correction & learner language · 156 works

Cite

Cite as Wajdi Zaghouani, Abdelhamid Ahmed, Xiao Zhang and Lameya Rezk. 2024. QCAW 1.0: Building a Qatari Corpus of Student Argumentative Writing. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024).

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

@inproceedings{zaghouani2024qcaw,
  title = {{QCAW 1.0: Building a Qatari Corpus of Student Argumentative Writing}},
  author = {Wajdi Zaghouani, Abdelhamid Ahmed, Xiao Zhang and Lameya Rezk},
  booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
  year = {2024},
  doi = {10.63317/382vs85mc4vh},
  url = {https://doi.org/10.63317/382vs85mc4vh}
}

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