WantWords

中|En

<p align="center"> <a href="https://wantwords.thunlp.org/"> <img src="resources/wantwords_logo.svg" width = "300" alt="WantWords Logo" /> </a> </p> <h3 align="center">An Open-source Online Reverse Dictionary [<a href="https://wantwords.net/">link</a>] </h3>

News

The WantWords MiniProgram has been launched. Welcome to scan the following QR code to try it!

<div align=center> <img src="resources/miniprogram.jpg" width = "300" alt="MiniProgram QR code"/> </div>

What Is a Reverse Dictionary?

Opposite to a regular (forward) dictionary that provides definitions for query words, a reverse dictionary returns words semantically matching the query descriptions.

<div align=center> <img src="resources/rd_example.png" alt="rd_example" width = "600"/> </div>

What Can a Reverse Dictionary Do?

• Solve the tip-of-the-tongue problem, the phenomenon of failing to retrieve a word from memory
• Help new language learners
• Help word selection (or word dictionary) anomia patients, people who can recognize and describe an object but fail to name it due to neurological disorder

Our System

Workflow

<div align=center> <img src="resources/workflow.png" alt="workflow" width = "500" /> </div>

Core Model

The core model of WantWords is based on our proposed Multi-channel Reverse Dictionary Model [paper] [code], as illustrate in the following figure.

<div align=center> <img src="resources/MRD_model.png" alt="model" width = "500" /> </div>

Pre-trained Models and Data

You can download and decompress the pre-trained models and data to BASE_PATH/website_RD/ to reimplement the system.

Key Requirements

• Django==2.2.5
• django-cors-headers==3.5.0
• numpy==1.17.2
• pytorch-transformers==1.2.0
• requests==2.22.0
• scikit-learn==0.22.1
• scipy==1.4.1
• thulac==0.2.0
• torch==1.2.0
• urllib3==1.25.6
• uWSGI==2.0.18
• uwsgitop==0.11

Cite

If the code or data help you, please cite the following two papers.

@inproceedings{qi2020wantwords,
  title={WantWords: An Open-source Online Reverse Dictionary System},
  author={Qi, Fanchao and Zhang, Lei and Yang, Yanhui and Liu, Zhiyuan and Sun, Maosong},
  booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  pages={175--181},
  year={2020}
}

@inproceedings{zhang2020multi,
  title={Multi-channel reverse dictionary model},
  author={Zhang, Lei and Qi, Fanchao and Liu, Zhiyuan and Wang, Yasheng and Liu, Qun and Sun, Maosong},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  pages={312--319},
  year={2020}
}