---
title: "Addressing Semantic Drift in Question Generation for"
description: "BLEU-style metrics reward only one reference wording, so the authors also propose a QA-based… 59 references, cited by 69 papers here."
authors: ["Shiyue Zhang", "Mohit Bansal"]
year: 2019
venue: "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)"
source: "https://aclanthology.org/D19-1253/"
doi: "10.18653/v1/d19-1253"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2019-addressing-semantic-drift-question-generation/"
---
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# Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering

**Authors:** [Shiyue Zhang](https://md.research.elamur.ai/free/en/authors/shiyue-zhang/) and [Mohit Bansal](https://md.research.elamur.ai/free/en/authors/mohit-bansal/) · **Year:** 2019 · **Venue:** [Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2019](https://md.research.elamur.ai/free/en/venues/emnlp/2019/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D19-1253/) · [PDF](https://aclanthology.org/D19-1253.pdf) · [DOI 10.18653/v1/d19-1253](https://doi.org/10.18653/v1/d19-1253) · [OpenAlex](https://openalex.org/W2970796366)

**Citations:** 105 citations (OpenAlex, 2026-10-14) · [69](https://md.research.elamur.ai/free/en/papers/zhang-2019-addressing-semantic-drift-question-generation/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Neural question generation (QG) models trained only with teacher forcing tend to produce questions whose meaning wanders away from the source passage and target answer. The authors call this "semantic drift." To counter it, they fine-tune a paragraph-level QG model with reinforcement learning using two rewards. QPP scores how likely the output paraphrases the reference question, and QAP scores how likely a QA model recovers the given answer from it. The base model extends Zhao et al. (2018) with POS/NER features, ELMo or BERT embeddings, and tied output embeddings. BLEU-style metrics reward only one reference wording, so the authors also propose a QA-based evaluation: they train a QA model on QG-produced data and measure how well it performs. The rewarded models set a new state of the art on SQuAD QG. The best generator is then used to create synthetic data for semi-supervised QA on SQuADv1.1. Two strategies make this data useful: a QAP-based filter and mixing mini-batch training. Both BiDAF and BERT QA baselines improve, even when no new articles are added.

## Key points
- With new Wikipedia articles, synthetic data improves EM/F1 by 1.69/1.27 for BiDAF-QA and 1.19/0.56 for BERT-QA. Using only extra beam-search questions on existing SQuAD contexts, the gains are 1.51/1.13 and 0.95/0.13.
- On the full SQuAD dev set, the BiDAF setup rises from 72.19/81.52 to 74.12/82.83 using 705k new examples. Dhingra et al. reached only 71.86/80.80 with 3.2M cloze pairs.
- With BERT-QG, the QAP-only model performs best overall (BLEU4 18.65, METEOR 22.91), outperforming the combined-reward variant.
- Benefits from synthetic data level off around H2–H4. Different beam sizes and diverse beam search give similar results, which suggests QG quality limits the gains.
- Code and models are released at github.com/ZhangShiyue/QGforQA.

*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 of North Carolina at Chapel Hill](https://md.research.elamur.ai/free/en/universities/university-of-north-carolina-at-chapel-hill/) — University, Chapel Hill, United States · 37 works · 14,332 citations

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🧭 Topics (1): [❓ Question answering](https://md.research.elamur.ai/free/en/topics/question-answering/) · 242 works

## Cite

Cite as: Shiyue Zhang and Mohit Bansal. 2019. Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP).

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

```bibtex
@inproceedings{zhang2019addressing,
  title = {{Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering}},
  author = {Shiyue Zhang and Mohit Bansal},
  booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
  year = {2019},
  doi = {10.18653/v1/d19-1253},
  url = {https://doi.org/10.18653/v1/d19-1253}
}
```

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*Updated 24 October 2026*