--- title: "AandP — paper by Tran (2020) — summary and ideas" description: "The author builds a small rule-based system in Prolog with Definite Clause Grammars (DCG), using extra arguments, extra… 6 references." authors: ["Trung Q. Tran"] year: 2020 venue: "arXiv:2001.05672" source: "https://arxiv.org/abs/2001.05672" doi: "10.48550/arxiv.2001.05672" arxiv: "2001.05672" 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/tran-2020-aandp-utilizing-prolog-converting-between/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/tran-2020-aandp-utilizing-prolog-converting-between/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2020](https://md.research.elamur.ai/free/en/papers/2020/page/10/) / AandP: Utilizing Prolog for converting between active sentence and passive sentence with three-steps conversion # AandP: Utilizing Prolog for converting between active sentence and passive sentence with three-steps conversion **Authors:** Trung Q. Tran · **Year:** 2020 · **Venue:** arXiv:2001.05672 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/) **Links:** [Publisher's page](https://arxiv.org/abs/2001.05672) · [PDF](https://arxiv.org/pdf/2001.05672) · [DOI 10.48550/arxiv.2001.05672](https://doi.org/10.48550/arxiv.2001.05672) · [arXiv 2001.05672](https://arxiv.org/abs/2001.05672) · [OpenAlex](https://openalex.org/W2999671404) **Citations:** 0 citations (OpenAlex, 2026-10-14) ## Summary The paper addresses a classic grammar task: turning an English active-voice sentence into its passive counterpart and back. The meaning stays the same while the structure changes. The author builds a small rule-based system in Prolog with Definite Clause Grammars (DCG), using extra arguments, extra goals and a hand-written lexicon. Coverage includes all 12 English tenses, modal verbs, negation, singular/plural agreement, subject/object pronoun forms and irregular past participles. Conversion is refused when the active sentence has no object. The author introduces three design elements: - a flattened sentence representation restricted to the transitive case; - an auxiliary-based grouping of the 12 tenses into 4 classes, which reduces the number of DCG rules; - a three-step pipeline: parse the sentence into a representation, rewrite that representation into the other voice, then generate the surface sentence from it. The result is a working, open-source program demonstrated on example queries. There is no quantitative evaluation. The work matters mainly as a transparent, reversible model of voice alternation, built so that the same grammar serves both analysis and generation. ## Key points - Implementation: Prolog + DCG. Code at github.com/tqtrunghnvn/ActiveAndPassive, split into `convertible.pl` (DCG rules and lexicon), `convert.pl` (the conversion step) and `testSuite.pl` (user commands). - Coverage includes: - 12 tenses, modals (e.g., *should* → *should be bought*) and negation with *not*; - do-support in negated simple tenses, number agreement and pronoun case swapping (*he* ↔ *him*); - irregular participles (*buy* → *bought*); - noun phrases with adjective lists and PP modifiers. - Passive auxiliaries are organised into three `auxTense` groups (*be*, *being*, *been*). Separately, the 12 tenses are grouped into 4 classes by their auxiliary pattern. - Representation: `s(NP, V, NP)` for actives and `s(NP, aux, auxTense, v, agent(by), NP)` for passives, with `pol(not)` and `modal(X)` as extra slots. The query interface is `convert(ActiveS, ActiveRe, PassiveS, PassiveRe)`. - Limitations: every word must be listed in the lexicon. Testing consists only of `activeTestSuite`/`passiveTestSuite` commands, with no reported accuracy figures. ## Interesting ideas - Voice change is done as parse → rewrite the structure → regenerate, with one reversible grammar used for both directions, so the same rules analyse and produce text. - Instead of 12 separate tense cases, tenses are grouped by the shape of their auxiliary chain, which collapses many rules into a few. - Passivisation is gated on the presence of an object, and the passive's subject and *by*-phrase are mapped back to the active's object and subject. - Agreement and pronoun case are threaded through the rules as extra arguments, so swapping roles automatically re-inflects the auxiliary and pronoun forms. - Two-direction test suites exercise the converter both ways, and sentences without an object are expected to be rejected. ## For Mova How these ideas could serve Mova, our small open language model — its files, stages and tests: - **I1** (back): frame-to-text generation could choose active or passive from a single frame, reusing the analysis grammar's knowledge as generation constraints rather than separate templates. - **I2** (tokens): tense/aspect/voice features could be computed from the aux chain pattern (modal? have? be+ing? be+participle?) as a compact, explainable morphology step. - **I3** (frames): normalise `nsubj:pass` and `obl:agent` back to patient/agent roles so that "an apple is bought by him" and "he buys an apple" yield the same who-did-what-to-whom frame. - **I4** (back): when generation promotes a different participant to subject, recompute verb agreement and pronoun case from the frame rather than copying surface forms. - **I5** (learn): use active↔passive round-trips as a consistency check on parsing and generation, and use intransitive sentences as a negative control that must fail to passivise. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* 📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works ## Cite Cite as: Trung Q. Tran. 2020. AandP: Utilizing Prolog for converting between active sentence and passive sentence with three-steps conversion. arXiv:2001.05672. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @misc{tran2020aandp, title = {{AandP: Utilizing Prolog for converting between active sentence and passive sentence with three-steps conversion}}, author = {Trung Q. Tran}, year = {2020}, doi = {10.48550/arxiv.2001.05672}, eprint = {2001.05672}, archivePrefix = {arXiv}, url = {https://doi.org/10.48550/arxiv.2001.05672} } ``` - [← previous A Thesaurus for Biblical Hebrew](https://md.research.elamur.ai/free/en/papers/azar-2020-thesaurus-biblical-hebrew/) - [next → Bibliography of Ethiopian Semitic, Cushitic and Omotic Linguistics XXII: 2018](https://md.research.elamur.ai/free/en/papers/bulakh-2020-bibliography-ethiopian-semitic-cushitic-omotic/) - [↑ back to papers of 2020](https://md.research.elamur.ai/free/en/papers/2020/page/10/) ## References 6 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Donald Davidson and Gilbert Harman. Semantics of natural language, volume 40. Springer Science & Business Media, 2012. [find](https://scholar.google.com/scholar?q=Donald+Davidson+and+Gilbert+Harman%2E+Semantics+of+natural+language%2C+volume+40%2E+Springer+Science+%26+Business+Media%2C+2012%2E) 2. Ralph Grishman. Computational linguistics: an introduction. Cambridge University Press, 1986. [find](https://scholar.google.com/scholar?q=Ralph+Grishman%2E+Computational+linguistics%3A+an+introduction%2E+Cambridge+University+Press%2C+1986%2E) 3. Pierre M Nugues. An introduction to language processing with perl and prolog. Springer, 2006. [↗](https://doi.org/10.1007/3-540-34336-9) 4. Fernando CN Pereira and Stuart M Shieber. Prolog and natural-language analysis. Microtome Publishing, 2002. [find](https://scholar.google.com/scholar?q=Fernando+CN+Pereira+and+Stuart+M+Shieber%2E+Prolog+and+natural%2Dlanguage+analysis%2E+Microtome+Publishing%2C+2002%2E) 5. Fernando CN Pereira and David HD Warren. Definite clause grammars for language analysis—a survey of the formalism and a comparison with augmented transition networks. Artificial intelligence, 13(3):231–278, 1980. [↗](https://doi.org/10.1016/0004-3702(80)90003-x) 6. John Stobo. Problem solving with Prolog. CRC Press, 2004. [↗](https://doi.org/10.4324/9781315272825) *Updated 1 November 2026*