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Exploiting parsed corpora in grammar teaching

Paper · 2019 · Sean Wallis, Ian Cushing and Bas Aarts

Summary

The paper asks how parsed corpora can support first-language English grammar teaching in schools in England. A national curriculum, in force since 2014, requires explicit grammatical knowledge, but most teachers never learned formal grammar and many distrust it. The authors contrast bottom-up "data-driven learning" (classroom concordancing, after Johns and King) with their own concept-driven, top-down approach. They argue that a corpus shows learners that a pattern occurs but not why, so access to a corpus without a knowledgeable teacher is not enough.

They present Englicious, a web platform that draws examples from the fully parsed ICE-GB corpus. It offers professional-development material, lessons and exercises organised concept by concept. It was built together with teachers and extended from secondary to primary level.

The central open technical issue is the "selection problem": how to choose corpus examples that suit a teaching purpose. Adult corpus sentences are often too complex for young pupils, so selection still relies partly on hand-crafting. The work matters as a model of how treebanks can serve education and not only research or NLP.

Key points

  • Englicious takes its data from ICE-GB, the parsed corpus that represents Great Britain within the worldwide ICE collection of English varieties. ICE-GB contains speech and writing, with context and possible audio playback.
  • The authors replicate Johns' (1991) persuade/convince exercise on ICE-GB. Only 1 of 15 to-clauses with either verb involves convince, against 26 of 39 that-clauses. In Johns' corpus, persuade took the "persuade someone to" pattern in 14 of 18 cases.
  • Unfiltered ICE-GB sentences suit only late secondary pupils. For younger children, the team had to pick examples by hand, edit them or write their own.
  • The authors liken the "selection problem" to the research-side "abstraction problem" of formulating corpus queries. Its goals differ, however, because teaching aims are not research aims.
  • Cited evidence includes a large randomised controlled trial (Myhill et al. 2012). It found that grammar taught in context as a writing "design tool" improved secondary pupils' writing, with the biggest gains among more able students. Evidence for primary-school benefits remains mixed.

Interesting ideas

  • I1. Selecting examples by purpose, not just by matching a query, means filtering parsed hits for complexity, relevance and audience before showing them.
  • I2. The paper insists on explaining why a word belongs to a category (e.g. why work is a noun in a given sentence) rather than just stating the label.
  • I3. Comparing the complement patterns of near-synonyms (to-clause vs that-clause after persuade/convince) in a corpus yields distributional evidence for verb subcategorisation.
  • I4. Johns stresses that a pattern missing from a corpus is still possible, and ICE-GB indeed contains "convince me to". This is a caution against treating unseen as ungrammatical.
  • I5. Corpus sentences are decontextualised performance, and readers need wider context (full sentences to whole passages) to interpret them.

For Mova

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

  • I1 (learn): Silver training and evaluation sets could be chosen by structural and readability criteria, giving graded curricula of sentences from easy to hard.
  • I2 (tokens): The tagger could attach a short justification to each tag (e.g. determiner before it, plural morphology), which fits Mova's explainability goal.
  • I3 (frames): Treebank counts of complement types could check or weight VerbNet frame assignments for verbs that have close synonyms.
  • I4 (learn): Self-induced repair rules should not rule out constructions just because the silver data lacks them; rare-but-valid cases belong among the negative controls.
  • I5 (answer): QA readers on fables and school texts should resolve questions against the surrounding passage, not isolated sentences, when building the world of the text.

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

🎓University College LondonLondon · United Kingdom88 works · 19,432 citations

Also on the byline: Brunel University of London.

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksPsychology · 1,552 worksDevelopmental and Educational Psychology · 499 works

Cite

Cite as Sean Wallis, Ian Cushing and Bas Aarts. 2019. Exploiting parsed corpora in grammar teaching. Linguistic Issues in Language Technology.

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

@inproceedings{wallis2019exploiting,
  title = {{Exploiting parsed corpora in grammar teaching}},
  author = {Sean Wallis, Ian Cushing and Bas Aarts},
  booktitle = {Linguistic Issues in Language Technology},
  year = {2019},
  doi = {10.33011/lilt.v18i.1437},
  url = {https://doi.org/10.33011/lilt.v18i.1437}
}

References

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