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# Question-Answer Driven Semantic Role Labeling: Using Natural Language to Annotate Natural Language

**Authors:** [Luheng He](https://md.mova.elamur.ai/free/en/docs/papers/authors/luheng-he/), [Mike Lewis](https://md.mova.elamur.ai/free/en/docs/papers/authors/mike-lewis/) and [Luke Zettlemoyer](https://md.mova.elamur.ai/free/en/docs/papers/authors/luke-zettlemoyer/) · **Year:** 2015 · **Venue:** Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing **Link:** [aclanthology.org/D15-1076](https://aclanthology.org/D15-1076/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only

[**197** citations](https://openalex.org/works?filter=cites:W2251199578) openalex · 2026-10-18[**127** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/he-2015-question-answer-driven-semantic-role/) of the 140673 papers whose bibliographies we read[Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing](https://aclanthology.org/volumes/D15-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/D15-1076/)
- [PDF](https://aclanthology.org/D15-1076.pdf)
- [DOI 10.18653/v1/d15-1076](https://doi.org/10.18653/v1/d15-1076)
- [OpenAlex](https://openalex.org/W2251199578)

**Authors (3)**

1. [Luheng He](https://openalex.org/A5025887224) h 26 · 6321 citations · 46 works
2. [Mike Lewis](https://openalex.org/A5004412943) [ORCID](https://orcid.org/0000-0003-0679-6612) · h 43 · 39413 citations · 84 works
3. [Luke Zettlemoyer](https://openalex.org/A5067919401) [ORCID](https://orcid.org/0009-0008-8296-0764) · h 93 · 82307 citations · 519 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The paper introduces question-answer driven semantic role labeling (QA-SRL): each verbal predicate is labeled with wh-questions, such as "Who finished something?", and each question is answered by phrases of the sentence. The questions come from a template of seven fields, so the roles are defined by the questions and not by a frame inventory. Non-expert annotators, hired on Upwork after a short screen, labeled over 3,000 sentences (nearly 8,000 verbs) at about 50 cents per verb. Agreement with PropBank is high: over all roles the precision is 81.4 and the recall 86.3, and for core arguments 85.9 and 89.8. Two baselines are given. Question generation uses a classifier over roles followed by templates; answer identification is a per-word classifier that finds the answer head, and it is right for about 80% of test questions, against a random baseline of about 26%.

## Ideas

- I1. Answer head as a deterministic rule on the UD tree: the head of an answer span is a word whose governor lies outside the span, as the paper defines it. Implement it in `en/src/parse.rs` and measure head accuracy against gold spans on UD EWT dev.
- I2. Map the wh-words to role classes in `prag/src/roles.rs`: who and what as core, when, where, why and how as adjuncts. Report the agreement with the school's role labels, in the form of the paper's Table 5.
- I3. The mapping table of wh-field and voice to roles (the paper's Table 7) is a closed table. Put it in [28. Voice and the passive](https://md.mova.elamur.ai/free/en/docs/languages/en/28-voice-and-passive/) and test it on active and passive school sentences.
- I4. Negative control: choose the answer head at random among the children of the verb. The paper's random baseline is about 26%; Mova's rule should beat it by a wide margin on the same sentences.

## For Mova

The chapters are [26. Questions](https://md.mova.elamur.ai/free/en/docs/languages/en/26-questions/) and [28. Voice and the passive](https://md.mova.elamur.ai/free/en/docs/languages/en/28-voice-and-passive/). The code is `en/src/parse.rs` and `prag/src/roles.rs`. The measures are the answer-head accuracy on UD EWT dev, the core and adjunct agreement on school sentences, and the random-choice control.

Topics: [Semantic role labeling](https://md.mova.elamur.ai/free/en/docs/papers/topics/semantic-role-labeling/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/d15-1076](https://doi.org/10.18653/v1/d15-1076)

```
@inproceedings{he2015question,
  title = {{Question-Answer Driven Semantic Role Labeling: Using Natural Language to Annotate Natural Language}},
  author = {He, Luheng and Lewis, Mike and Zettlemoyer, Luke},
  booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
  year = {2015},
  doi = {10.18653/v1/d15-1076},
  url = {https://doi.org/10.18653/v1/d15-1076}
}
```

- [← previous Automatic Generation of English Respellings](https://md.mova.elamur.ai/free/en/docs/papers/hauer-2013-automatic-generation-english-respellings/)
- [next → Deep Semantic Role Labeling: What Works and What's Next](https://md.mova.elamur.ai/free/en/docs/papers/he-2017-deep-semantic-role-labeling-what/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

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