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The Bahrain Corpus: A Multi-genre Corpus of Bahraini Arabic

Summary

Most Arabic NLP resources cover Modern Standard Arabic. The few large Gulf Arabic collections, such as the Gumar Corpus, come almost entirely from one genre: conversational novels posted on online forums. This paper builds a dedicated, genre-diverse corpus of the Bahraini dialect. It contains about 620K words from 112 documents. Roughly two thirds are transcripts of spoken material, mostly YouTube recordings of local TV programmes such as comedies, dramas, plays, interviews, talk shows and cooking shows. The rest is originally written text, including folktales, songs, recipes and social commentary. Bahraini college students produced the transcriptions from 2016 onward, some as volunteers and some as coursework. The full corpus was then tagged automatically with a state-of-the-art Gulf Arabic morphosyntactic disambiguator (Inoue et al., 2022). The quality of that tagging was checked against a manually reviewed sample of about 7.6K words. The authors release the corpus and the annotated sample publicly. The resource matters because dialect tools need training and test data that go beyond a single register, and because the corpus also documents Bahraini language and culture.

Key points

  • Corpus size, from Table 1: 620,301 words, 59,846 unique words, 46,030 sentences and 112 documents. Words are counted by whitespace splitting, and each line counts as one sentence.
  • Transcribed speech makes up 66.0% of the corpus. It covers both scripted and unscripted TV genres. The remainder is written text.
  • Morphological annotation is fully automatic and uses the pre-trained language model tagger of Inoue et al. (2022). It is validated on a 7.6K-word sample.
  • The paper describes two main local varieties: a general Gulf-like Bahraini variety and Baḥrāni Arabic. Each has its own city-level accents. The corpus does not annotate sub-dialects.
  • Non-standard spelling is a core difficulty, since one phrase can appear in several written forms. CODA is the field's proposed convention for handling this. The corpus is available at bahraincorpus.com.

Interesting ideas

  • I1. Build the corpus to balance genres and spoken versus written registers, instead of collecting as much text as possible from one convenient source, because single-genre data skews what tools learn. — For Mova (learn): Mova's silver and evaluation sets could be stratified by genre (fables, school tests, dialogue, recipes), so that parser and reader scores are reported per genre rather than as one average.
  • I2. Annotate the whole corpus automatically, then measure its quality on a small hand-checked slice. This shows users how trustworthy the bulk labels are without paying for full manual annotation. — For Mova (learn): every silver batch agreed by the witness parsers could come with a small gold-checked sample whose error rate is published alongside it.
  • I3. Treat spelling variation as a problem with its own explicit convention (CODA-style normalization), rather than leaving variant forms as unrelated words. — For Mova (tokens): the en crate could map informal or variant spellings in user and dialogue text to a canonical form before tagging, while keeping the original form for explanation.
  • I4. State exactly how basic counts are defined, here whitespace words and lines as sentences, so that statistics are reproducible and comparable across resources. — For Mova (tree): treebank and conversion reports could always say which tokenization and sentence-splitting rules produced their counts and LAS figures.
  • I5. Within the spoken material, keep scripted and unscripted speech apart, since they differ in how natural and how disfluent the language is. — For Mova (frames): tagging dialogue sources as scripted or spontaneous would let speech-act and implicature handling be evaluated separately on planned versus spontaneous exchanges.

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

Topics: Semitic languages

Cite

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

@inproceedings{abdulrahim2022bahrain,
  title = {{The Bahrain Corpus: A Multi-genre Corpus of Bahraini Arabic}},
  author = {Abdulrahim, Dana and Inoue, Go and Shamsan, Latifa and Khalifa, Salam and Habash, Nizar},
  booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference},
  year = {2022},
  doi = {10.63317/2ve5wix9jgsv},
  url = {https://doi.org/10.63317/2ve5wix9jgsv}
}

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