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# Domain-Aware Dependency Parsing for Questions

**Authors:** Aparna Garimella, Laura Chiticariu and Yunyao Li · **Year:** 2021 · **Venue:** Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 **Link:** [aclanthology.org/2021.findings-acl.400](https://aclanthology.org/2021.findings-acl.400/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**3** citations](https://openalex.org/works?filter=cites:W3175549965) openalex-snapshot · 2026-10-14[**1** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/garimella-2021-domain-aware-dependency-parsing-questions/) of the 140673 papers whose bibliographies we read[Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021](https://aclanthology.org/volumes/2021.findings-acl/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2021.findings-acl.400/)
- [PDF](https://aclanthology.org/2021.findings-acl.400.pdf)
- [DOI 10.18653/v1/2021.findings-acl.400](https://doi.org/10.18653/v1/2021.findings-acl.400)
- [OpenAlex](https://openalex.org/W3175549965)

**Authors (3)**

1. [Aparna Garimella](https://openalex.org/A5014285077) [ORCID](https://orcid.org/0000-0003-3111-0686) · [Semantic Scholar](https://www.semanticscholar.org/author/31099365) · h 10 · 448 citations · 91 works
2. [Laura Chiticariu](https://openalex.org/A5050310059) [Semantic Scholar](https://www.semanticscholar.org/author/1779119) · h 19 · 2385 citations · 68 works
3. [Yunyao Li](https://openalex.org/A5102944075) [ORCID](https://orcid.org/0009-0002-0814-4634) · [Semantic Scholar](https://www.semanticscholar.org/author/2382048770) · h 18 · 1816 citations · 104 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

Pre-trained dependency parsers are trained on corpora where questions are rare (0.5% of Wall Street Journal sentences), so they fail on domain questions such as "Show me Neil's insider transactions since 2011". The authors generate labelled domain questions from a few seed questions that carry UD parses. Each seed becomes a template in which the subject and modifier entities are placeholders, and the templates are refilled from a small domain schema and knowledge base. A one-time schema annotation (connective prepositions, heading properties, class-dependency, possible wh-words) removes the unnatural outputs. Each domain has 250 hand-parsed questions, evaluated with 5-fold cross-validation. Training on the generated questions raises LAS by 4.9% to 9% over UD plus the general QuestionBank, and 10 seed questions already give 2.03% (Finance) and 2.82% (Weather) LAS gains with Malt. The schema annotations add 0.4% to 0.94% LAS.

## Ideas

- I1. `en/src/parse.rs` error analysis first: take the two failure types the paper names (a temporal noun attached as `dobj` instead of `nmod:tmod`; a noun phrase attached to the wrong verb in a "show me X since Y" question). Count them in the question tests before adding any template.
- I2. Template abstraction as a rule over the tree: replace the `nsubj` of the root and the `nmod` dependents with typed placeholders, and keep the rest of the tree. Store templates in a BTreeMap keyed by the abstracted tree, so generation is deterministic.
- I3. Schema annotations as lexicon data: for each class relation, record the connective preposition ("in" for a person in a company). This is a table in `world/src/knowledge.rs`, and it fixes the preposition errors the paper counts (68 of 100 samples).
- I4. Measure: the held-out split must be by template, as in the paper, so the same template never appears in train and test. Run the parser on the school questions twice and check the output is identical; add a negative control where the filler is shuffled, which must score worse.

## For Mova

The question side belongs to [26. Questions](https://md.mova.elamur.ai/free/en/docs/languages/en/26-questions/); the whose-expansion of a modifier fits [25. Relative clauses](https://md.mova.elamur.ai/free/en/docs/languages/en/25-relative-clauses/); the temporal modifier fits [07. Punctuation, capital letters, abbreviations and numbers](https://md.mova.elamur.ai/free/en/docs/languages/en/07-punctuation-capitals-numbers/) only for dates. Code: `en/src/parse.rs` (the attachment errors), `world/src/knowledge.rs` (schema annotations as school course data), `world/src/qa.rs` (question forms). Measure: UAS and LAS on a held-out set of school-domain questions, split by template, with the UD EWT dev score as a check that nothing regressed.

Topics: [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2021.findings-acl.400](https://doi.org/10.18653/v1/2021.findings-acl.400)

```
@inproceedings{garimella2021domain,
  title = {{Domain-Aware Dependency Parsing for Questions}},
  author = {Garimella, Aparna and Chiticariu, Laura and Li, Yunyao},
  booktitle = {Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021},
  year = {2021},
  doi = {10.18653/v1/2021.findings-acl.400},
  url = {https://doi.org/10.18653/v1/2021.findings-acl.400}
}
```

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## References

12 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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