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https://github.com/darkalexwang/ai_couplet

Machine Learning Trained Chinese Couplets / 用AI对对联
https://github.com/darkalexwang/ai_couplet

couplet encoder-decoder-attention machine-learning nlp seq2seq tensorflow2

Last synced: about 2 months ago
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Machine Learning Trained Chinese Couplets / 用AI对对联

Host: GitHub
URL: https://github.com/darkalexwang/ai_couplet
Owner: DarkAlexWang
License: mit
Created: 2021-11-08T05:16:58.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2024-03-13T21:31:12.000Z (10 months ago)
Last Synced: 2024-03-13T22:38:29.887Z (10 months ago)
Topics: couplet, encoder-decoder-attention, machine-learning, nlp, seq2seq, tensorflow2
Language: Jupyter Notebook
Homepage:
Size: 31 MB
Stars: 0
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # AI-couplet-writer

Welcome! This is our fun project of using AI to write couplets (对对联). Please check out our website!

https://ai-poet.com

## Introduction

- What is couplet (对联)

  - Couplet is a two-liner poem with strict semantic and phoenetic rules

  - See the [Wikipedia](https://en.wikipedia.org/wiki/Antithetical_couplet) (English) page for more details

- What is this project for

  - The model will predict the second line (下联) given the first line (上联)

  - This project improves from similar ones (see Reference for their great work) on:

    - This is the first of its kind written in TensorFlow 2, to the best of our knowledge

    - This model improves from the reference models mostly on (i) emotional (意境) matching; (ii) repeated characters treatment (see the **Model** section below)

## Examples

|     Input     |     Output    |

| ------------- | ------------- |

| 欲把心事付瑶琴  |  且将笔墨书诗画  |

| 半衾幽梦香初散  |  一曲清音韵未央  |

| 应是飞鸿踏泥雪  |  何如落雁归故乡  |

| 云破月来花弄影  |  雨停风送柳含烟  |

| 别后相思空一水  |  愁中寂寞又几回  |

| 书中自有黄金屋  |  笔下常留白玉簪  |

| 花谢花飞花满天  |  鸟啼鸟语鸟鸣春  |

| 杨柳岸晓风残月  |  芦苇春寒雨乱烟  |

## Model

This is a sequence-to-sequence model with Encoder + Decoder + Attention, schematically shown as below.

![The AI Couplet Model](/doc/schematics.png)

Notable features

- Embedding layer pretrained via Word2Vec

- Beam search decoder

- Format polishing to prevent repeated characters, etc.

## Data & How to Run

- Download the training data (~70k couplets) from [this](https://github.com/wb14123/couplet-dataset) repo

- Set up the model hyperparameters and the file paths in the **baseconfig.ini**

- Install the package dependencies

  - **TensorFlow 2.7.0**

  - Python 3.7.9

  - Numpy 1.19.5

  - Gensim 4.1.2

  - Scikit-Learn 0.24.2

- Train the model

  - Run as ```python train.py```

  - The model parameters will be written to the path set up in the config file

  - Our model on the website was trained on Google Colab Pro for 12 epochs (~1 day)

## Reference

- Seq2seq-couplet by Bin Wang ([Github](https://github.com/wb14123/seq2seq-couplet), [website](https://ai.binwang.me/couplet))

- Seq2seq chinese poetry generation by Simon and Vera ([Github](https://github.com/Disiok/poetry-seq2seq), [Related Paper](https://arxiv.org/abs/1610.09889))

- Microsoft Research/微软亚洲研究院电脑对联系统 ([Website](https://duilian.msra.cn/app/couplet.aspx))

- Open Couplet/中文对联AI ([Github](https://github.com/neoql/open_couplet), [Website](https://couplet.neoql.me/))

- AICHPOEM.COM/诗三百-人工智能诗歌平台 ([Website](https://www.aichpoem.com/#/shisanbai/poem))

- 清华九歌-人工智能诗歌写作系统 ([Website](http://jiuge.thunlp.org/jueju.html))