README.md

---
title: CTC_Eval
datasets:
-  
tags:
- evaluate
- metric
description: "This repo contains code of an automatic evaluation metric described in the paper
Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation"
sdk: gradio
sdk_version: 3.0.2
app_file: app.py
pinned: false
---

# Metric Card for CTC_Eval

## Metric Description
* Previous work on NLG evaluation has typically focused on a single task and developed individual evaluation metrics based on specific intuitions.
* In this work, we propose a unifying perspective based on the nature of information change in NLG tasks, including compression (e.g., summarization), transduction (e.g., text rewriting), and creation (e.g., dialog).
* A common concept underlying the three broad categories is information alignment, which we define as the extent to which the information in one generation component is grounded in another.
* We adopt contextualized language models to measure information alignment.

## How to Use
Example:
```python

    >>> ctc_score = evaluate.load("yzha/ctc_eval")
    >>> results = ctc_score.compute(references=['hello world'], predictions='hi world')
    >>> print(results)
    {'ctc_score': 0.5211202502250671}
```

### Inputs
- **input_field** 
    - `references`: The document contains all the information
    - `predictions`: NLG model generated text

### Output Values

The CTC Score. 

## Citation
@inproceedings{deng2021compression,
  title={Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation},
  author={Deng, Mingkai and Tan, Bowen and Liu, Zhengzhong and Xing, Eric and Hu, Zhiting},
  booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
  pages={7580--7605},
  year={2021}
}