---
title: "Chinese Poetry Generation with Recurrent Neural — Zhang 2014"
description: "The paper addresses the automatic composition of classical Chinese quatrains."
authors: ["Xingxing Zhang", "Mirella Lapata"]
year: 2014
venue: "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)"
source: "https://aclanthology.org/D14-1074/"
doi: "10.3115/v1/d14-1074"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2014-chinese-poetry-generation-recurrent-neural/"
---
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# Chinese Poetry Generation with Recurrent Neural Networks

**Authors:** [Xingxing Zhang](https://md.research.elamur.ai/free/en/authors/xingxing-zhang/), [Mirella Lapata](https://md.research.elamur.ai/free/en/authors/mirella-lapata/) · **Year:** 2014 · **Venue:** Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/D14-1074/) · [PDF](https://aclanthology.org/D14-1074.pdf) · [DOI 10.3115/v1/d14-1074](https://doi.org/10.3115/v1/d14-1074) · [OpenAlex](https://openalex.org/W2115221470)

**Citations:** 296 citations (openalex-snapshot · 2026-10-14) · [80](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper addresses the automatic composition of classical Chinese quatrains. These are four-line poems with five or seven characters per line, and they must obey rhyme and Ping/Ze tonal patterns while still reading as coherent, evocative text. Earlier systems relied on templates, genetic search, SMT-style line-to-line translation or summarization, and they usually conditioned only on the preceding line or on n-grams. Zhang and Lapata propose RNNPG, a neural generator that handles content selection and surface realization together.

User keywords are expanded through the ShiXueHanYing phrase taxonomy, and the first line is assembled from those phrases under tonal constraints. Each subsequent line is produced character by character, conditioned on the whole poem so far. Three components do this work: a convolutional sentence model encodes earlier lines, a recurrent context model summarizes them, and a recurrent generation model predicts the next character.

On automatic metrics (perplexity, BLEU) and in a human study with Chinese poetry experts, RNNPG beats competitive SMT-based, summarization-based and random baselines. This was an early demonstration that neural networks can directly generate constrained creative text.

## Key points
- **Architecture.** The model has three parts:
  - a convolutional sentence model (CSM) turns each previous line into a vector;
  - a recurrent context model (RCM) builds position-specific context vectors from all prior lines;
  - a recurrent generation model (RGM) outputs the next-character distribution.
- **First-line generation.** Keywords are restricted to the ShiXueHanYing taxonomy, which has 1,016 manually built phrase clusters. Candidate first lines are ranked by a character-based RNN language model interpolated with a Kneser-Ney trigram and decoded with a stack decoder.
- **Automatic evaluation.** The BLEU-2 comparison is against a re-implementation of He et al.'s (2012) SMT generator. That SMT baseline was trained on 284,899 poems, whereas Yan et al. used 61,960. RNNPG also incorporates three of the SMT features.
- **Human evaluation.**
  - Design: 30 experts rated poems on fluency, coherence, meaningfulness and poeticness, and also gave an overall ranking.
  - Against baselines: for 5-char quatrains, RNNPG was significantly better than Random, SUM (Yan et al., 2013) and SMT.
  - Against human poems: RNNPG's overall rank was not significantly different from human poems, although human poems won on every other dimension.
- **Difficulty by form.** 7-char quatrains proved harder, and the gap between human and machine poems was wider for them.

## Interesting ideas
- **Encode all earlier text, not just the last line.** Summarizing every previously generated line into a context vector, instead of conditioning on the immediately preceding one, gives a simple recipe for long-range coherence. It could carry over to multi-sentence generation such as summaries or dialogue.
- **Separate hard constraints from soft fluency.** The system enumerates only candidates that satisfy the tonal pattern, then lets a language model rank them. This pattern suits any generation task with strict formal rules, such as meter, morphology agreement or templates.
- **Use a curated resource as a strong random baseline.** The Random system picks phrases from human-edited clusters tied to the keywords. A baseline like this checks whether a model adds value beyond its lexical resource.
- **Rank-based human scoring.** Converting each system's position in a ranked list into a normalized pairwise-win score complements per-dimension ratings. It helps when absolute scales are noisy across annotators.
- **Treat BLEU with caution for creative output.** The authors note that n-gram overlap can be overly conservative for creative tasks. They pair it with perplexity and expert judgments, which is a sensible evaluation mix for any open-ended generation.

*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: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/)

## Cited by

Cited by

80 papers of our library cite it (of the 140884 whose bibliographies we read); 5 of them have a page here, 75 not yet.

- [Neural Summarization by Extracting Sentences and Words](https://md.research.elamur.ai/free/en/papers/cheng-2016-neural-summarization-extracting-sentences-words/)
- [Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems](https://md.research.elamur.ai/free/en/papers/wen-2015-semantically-conditioned-lstm-based-natural/)
- [Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking](https://md.research.elamur.ai/free/en/papers/wen-2015-stochastic-language-generation-dialogue-recurrent/)
- [FUDGE: Controlled Text Generation With Future Discriminators](https://md.research.elamur.ai/free/en/papers/yang-2021-fudge-controlled-text-generation-future/)
- [Sentence Simplification with Deep Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/zhang-2017-sentence-simplification-deep-reinforcement-learning/)

## Cite

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

```bibtex
@inproceedings{zhang2014chinese,
  title = {{Chinese Poetry Generation with Recurrent Neural Networks}},
  author = {Zhang, Xingxing and Lapata, Mirella},
  booktitle = {Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2014},
  doi = {10.3115/v1/d14-1074},
  url = {https://doi.org/10.3115/v1/d14-1074}
}
```

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## References

30 references, 4 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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