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Singlish Where Got Rules One? Constructing a Computational Grammar for Singlish

Paper · 2022 · Siew Yeng Chow and Francis Bond

Summary

Singlish (Colloquial Singapore English) is often dismissed locally as "broken English," and it had no computational grammar. The authors set out to show that it follows systematic rules by encoding it formally. They built a Singlish branch of the English Resource Grammar (ERG), a broad-coverage HPSG grammar. They added a Singlish lexicon, new lexical types, and new or modified rules. The main targets were sentence-final particles (SFPs), whose relative order is constrained by treating each particle as a head that selects the clause before it. They also handled noun phrases where number is left unspecified and where determiners can be dropped.

To evaluate the grammar, they assembled a Singlish test suite from online sources such as Wiktionary and the NUS SMS Corpus. They then compared its parses with gold trees and with the output of standard ERG. The result is the first working computational grammar of Singlish, released together with a treebank under an open license. This gives a base for future work on contact varieties.

Key points

  • The grammar is a branch of the ERG, written in the HPSG framework. Grammar, treebank and corpus are released at github.com/siewyeng/SinglishERG under the MIT license.
  • SFPs are analysed as heads that take sentences as complements through a dedicated Complement-Head Rule.
    • Particles fall into four positional types. A type-n particle selects a sentence already closed by a lower-type particle (S n−1).
    • This blocks bad orders such as "*like that làh one."
  • Particle tone is not written in the orthography. Instead, it is recorded in the lexical entry as a number roughly matching Hanyu Pinyin tones. For example, low ǎh and rising áh are separate entries.
  • A new Singlish Noun Inflectional Rule gives bare nouns an underspecified person-number value of 3, so they can agree with "those." It replaces the standard singular rule rather than being added alongside it, which avoids duplicate analyses. Determinerless count nouns ("use computer") are also allowed.
  • Planned work: a larger Singlish vocabulary and better efficiency, since dropping agreement and allowing uninflected verbs makes parsing more ambiguous. Suggested data sources include the NUS SMS Corpus, ICE-SIN, r/Singapore, Hardware Zone and the upcoming CoSEM.

Interesting ideas

  • I1. Treating stacked discourse particles as heads makes their ordering fall out of complement selection, with no need for separate linear-order filters.
  • I2. Numbered slot classes (S0 → S1 → … → S4) describe where each particle may occur relative to the others, which is a compact way to encode ordered pragmatic layers.
  • I3. When adding a dialect feature, the authors replaced the standard singular-noun rule with an underspecified one rather than adding a parallel rule, so ambiguity does not grow.
  • I4. Information missing from the spelling, such as particle tone, is stored as a feature in the lexical entry. Homographs are turned off except when specifically under test.
  • I5. The dialect grammar is a branch of a mature base grammar and is scored on the same gold trees as the unmodified base. This shows exactly what the extension adds.

For Mova

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

  • I1 (tree): Clause-final tags in informal English ("..., right?", "..., though") could get a constrained attachment scheme, so the parser rejects impossible stacks instead of attaching each tag independently.
  • I2 (frames): Speech-act and stance markers could be ranked into layers, so prag reads the outermost marker as the final force of the utterance and inner ones as modifiers.
  • I3 (tokens): Morphology for informal or non-native text could swap rules for a whole dialect profile instead of stacking alternatives, which keeps lemma and number analyses from multiplying.
  • I4 (tokens): Lexicon entries could carry hidden disambiguating features for homographs, with a switch that enables all readings only in diagnostic runs.
  • I5 (learn): Any dialect or repair-rule extension should be evaluated side by side with the untouched baseline as a control, so gains are attributed to the change and not to the data.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Nanyang Technological UniversitySingapore · Singapore156 works · 25,644 citations

 Fields, topics and concepts

📚 Shelves 4The 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 works

Cite

Cite as Siew Yeng Chow and Francis Bond. 2022. Singlish Where Got Rules One? Constructing a Computational Grammar for Singlish. Proceedings of the Thirteenth Language Resources and Evaluation Conference.

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

@inproceedings{chow2022singlish,
  title = {{Singlish Where Got Rules One? Constructing a Computational Grammar for Singlish}},
  author = {Siew Yeng Chow and Francis Bond},
  booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference},
  year = {2022},
  doi = {10.63317/43nzfbts9d88},
  url = {https://doi.org/10.63317/43nzfbts9d88}
}

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