---
title: "Constrained Decoding for Neural NLG from Compositional"
description: "Neural sequence-to-sequence generators for task-oriented dialogue usually take flat slot-value… 49 references, cited by 62 papers here."
authors: ["Anusha Balakrishnan", "Jinfeng Rao", "Kartikeya Upasani", "Michael White", "Rajen Subba"]
year: 2019
venue: "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/P19-1080/"
doi: "10.18653/v1/p19-1080"
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/balakrishnan-2019-constrained-decoding-neural-nlg-compositional/"
---
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# Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue

**Authors:** [Anusha Balakrishnan](https://md.research.elamur.ai/free/en/authors/anusha-balakrishnan/), [Jinfeng Rao](https://md.research.elamur.ai/free/en/authors/jinfeng-rao/), [Kartikeya Upasani](https://md.research.elamur.ai/free/en/authors/kartikeya-upasani/), [Michael White](https://md.research.elamur.ai/free/en/authors/michael-white/) and [Rajen Subba](https://md.research.elamur.ai/free/en/authors/rajen-subba/) · **Year:** 2019 · **Venue:** [Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2019](https://md.research.elamur.ai/free/en/venues/acl/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/P19-1080/) · [PDF](https://aclanthology.org/P19-1080.pdf) · [DOI 10.18653/v1/p19-1080](https://doi.org/10.18653/v1/p19-1080) · [OpenAlex](https://openalex.org/W2949413855)

**Citations:** 70 citations (OpenAlex, 2026-10-14) · [62](https://md.research.elamur.ai/free/en/papers/balakrishnan-2019-constrained-decoding-neural-nlg-compositional/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Neural sequence-to-sequence generators for task-oriented dialogue usually take flat slot-value meaning representations (MRs) as input. Such MRs say nothing about discourse structure, so generators cannot be told when to contrast, justify or group information, and they often omit or hallucinate content. Balakrishnan et al. revive an idea from rule-based NLG: hierarchical MRs built from arguments, dialog acts (e.g. INFORM, RECOMMEND) and nestable discourse relations (JOIN, JUSTIFY, CONTRAST). The model produces a bracketed tree alongside the text. This lets the authors apply constrained decoding, which forces the output tree to cover exactly the input content in a valid structure. They also release two resources: a new crowd-annotated weather-domain dataset collected in a conversational setting, and an E2E dataset enriched with tree-structured MRs. On both datasets, tree-based inputs combined with constrained decoding improve semantic correctness, control over CONTRAST, and diversity over flat baselines. They also make models more data-efficient and help them generalize to compositional structures. The work shows that adding explicit structure to neural NLG yields more controllable and reliable dialogue responses.

## Key points
- **Weak contrast in E2E systems:** In the E2E challenge, only 43 of 10,080 system outputs (0.4%) contained contrastive tokens, although the test set has about 300 contrastive references. This motivates explicit discourse relations in the input.
- **Data efficiency:** S2S-Constr (tree MRs plus constrained decoding) reaches over 90% tree accuracy with only 2K training examples and over 95% with 5K, on both the E2E and weather data.
- **Compositional generalization:** The E2E training set was split into 26,896 flat and 24,530 compositional examples. When 5% of the compositional examples were added to training, a model also trained on weather data reached 76% tree accuracy, compared with 53.72% for the E2E-only model.
- **Diversity:** On E2E, S2S-Constr produces 134 unique tokens, 1,128 unique trigrams and a Shannon entropy of 5.86. The flat-input baselines TGen (83 / 597 / 5.41) and SLUG (74 / 507 / 5.35) score lower.
- **Error analysis and release:** In 35 cases where tree accuracy and human judgments disagreed, 63% were traced to noise in the automatic E2E annotation. Code and datasets are available at github.com/facebookresearch/TreeNLG.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, Michael White and Rajen Subba. 2019. Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.

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

```bibtex
@inproceedings{balakrishnan2019constrained,
  title = {{Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue}},
  author = {Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, Michael White and Rajen Subba},
  booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
  year = {2019},
  doi = {10.18653/v1/p19-1080},
  url = {https://doi.org/10.18653/v1/p19-1080}
}
```

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*Updated 24 October 2026*