---
title: "Variational Neural Discourse Relation — Zhang 2016"
description: "Implicit discourse relations hold between two adjacent text spans (Arg1 and Arg2) that have no explicit connective such as \"because\"."
authors: ["Biao Zhang", "Deyi Xiong", "Jinsong Su", "Qun Liu", "Rongrong Ji", "Hong Duan", "Min Zhang"]
year: 2016
venue: "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D16-1037/"
doi: "10.18653/v1/d16-1037"
arxiv: "1603.03876"
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-2016-variational-neural-discourse-relation-recognizer/"
---
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# Variational Neural Discourse Relation Recognizer

**Authors:** [Biao Zhang](https://md.research.elamur.ai/free/en/authors/biao-zhang/), [Deyi Xiong](https://md.research.elamur.ai/free/en/authors/deyi-xiong/), [Jinsong Su](https://md.research.elamur.ai/free/en/authors/jinsong-su/), [Qun Liu](https://md.research.elamur.ai/free/en/authors/qun-liu/), [Rongrong Ji](https://md.research.elamur.ai/free/en/authors/rongrong-ji/), [Hong Duan](https://md.research.elamur.ai/free/en/authors/hong-duan/), [Min Zhang](https://md.research.elamur.ai/free/en/authors/min-zhang/) · **Year:** 2016 · **Venue:** Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D16-1037/) · [PDF](https://aclanthology.org/D16-1037.pdf) · [DOI 10.18653/v1/d16-1037](https://doi.org/10.18653/v1/d16-1037) · [arXiv 1603.03876](https://arxiv.org/abs/1603.03876) · [OpenAlex](https://openalex.org/W2963918167)

**Citations:** 22 citations (openalex-snapshot · 2026-10-14) · [8](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Implicit discourse relations hold between two adjacent text spans (Arg1 and Arg2) that have no explicit connective such as "because". They are hard to detect because the surface text gives almost no cue. Earlier systems treat this as discriminative classification over hand-crafted features or learned sentence embeddings, which can overfit given limited and unbalanced data.

The authors take a generative approach instead. Their model, VarNDRR, assumes that a continuous latent semantic variable produces both the argument pair and its relation label. The design borrows from the variational autoencoder. A neural "recognizer" reconstructs bag-of-words versions of both arguments and predicts the relation. Two neural "approximators" estimate a Gaussian posterior and a Gaussian prior over the latent variable. Training uses a reparameterized variational lower bound with ordinary stochastic gradient methods.

On the PDTB-style English benchmark, the model reaches F1 scores comparable to strong state-of-the-art baselines while using no manual features. The authors present this as the first generative graphical model for implicit discourse relation recognition (DRR).

## Key points
- **Data and setup:** Experiments use the Penn Discourse Treebank (PDTB). Implicit DRR is split into four separate tasks, one per relation type, and analysis suggests this split works better than classifying a mixture of relations.
- **Input and reconstruction:**
  - Arg1 and Arg2 are encoded as bag-of-words vectors.
  - Reconstruction treats each argument as a multivariate Bernoulli distribution.
  - The relation is predicted by a softmax layer on top of a four-layer MLP applied to the latent variable z.
- **Inference networks:** The posterior q(z|x,y) and prior q(z|x) are both diagonal Gaussians produced by feed-forward networks. The reparameterization trick connects the inference networks to the generative part so the whole model trains end to end.
- **Relation to prior work:** The closest work is the latent variable RNN of Ji et al. (2016). VarNDRR differs in three ways:
  - it uses simple feed-forward encoders rather than recurrent ones;
  - its latent variable is a semantic representation, not the relation label itself;
  - it is optimized with variational inference rather than plain likelihood.
- **Future directions:** The authors propose using explicit-connective instances, either converted into pseudo-implicit examples or exploited through semi-supervised learning. They also suggest applying the model to tasks like natural language inference.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Discourse analysis](https://md.research.elamur.ai/free/en/topics/discourse-analysis/)

## Cited by

Cited by

8 papers of our library cite it (of the 140884 whose bibliographies we read); 3 of them have a page here, 5 not yet.

- [Automatic Prediction of Discourse Connectives](https://md.research.elamur.ai/free/en/papers/malmi-2018-automatic-prediction-discourse-connectives/)
- [Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings](https://md.research.elamur.ai/free/en/papers/nguyen-2019-employing-correspondence-relations-connectives-identify/)
- [Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification](https://md.research.elamur.ai/free/en/papers/qin-2017-adversarial-connective-exploiting-networks-implicit/)

## Cite

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

```bibtex
@inproceedings{zhang2016variational,
  title = {{Variational Neural Discourse Relation Recognizer}},
  author = {Zhang, Biao and Xiong, Deyi and Su, Jinsong and Liu, Qun and Ji, Rongrong and Duan, Hong and Zhang, Min},
  booktitle = {Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing},
  year = {2016},
  doi = {10.18653/v1/d16-1037},
  eprint = {1603.03876},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/d16-1037}
}
```

- [← previous Variabilité des syllabes réalisées par des apprenants de l'anglais (Analysing syllable variability in a French learner corpus of English)](https://md.research.elamur.ai/free/en/papers/ballier-2016-variabilite-des-syllabes-realisees-par/)
- [next → Verb Phrase Ellipsis Resolution Using Discriminative and Margin-Infused Algorithms](https://md.research.elamur.ai/free/en/papers/aclbulk-2016-dean-verb-phrase-ellipsis-resolution-discriminative/)
- [↑ back to papers of 2016](https://md.research.elamur.ai/free/en/papers/2016/page/4/)

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