MaTESe: Machine Translation Evaluation as a Sequence Tagging Problem

This repository contains the implementation of the MaTESe metrics, which have been introduced in the paper "MaTESe: Machine Translation Evaluation as a Sequence Tagging Problem" presented at WMT 2022 (read it here).

NOTE: the checkpoints in this repository correspond to the metrics submitted to WMT 2023 (except for MaTESe-QE, that has been re-trained but not re-submitted).

About MaTESe

MaTESe metrics tag the error spans of a translation, assigning to them a level of severity that can be either 'Major' or 'Minor'. Additionally, the evaluation produces a numerical quality score, which is derived from combining the penalties linked to each error span. We have created two metrics: MaTESe and MaTESe-QE. The former requires references to conduct the evaluation, whereas the latter enables a reference-free evaluation.

If you find our paper or code useful, please reference this work in your paper:

@inproceedings{perrella-etal-2022-matese,
    title = "{M}a{TES}e: Machine Translation Evaluation as a Sequence Tagging Problem",
    author = "Perrella, Stefano  and
      Proietti, Lorenzo  and
      Scir{\`e}, Alessandro  and
      Campolungo, Niccol{\`o}  and
      Navigli, Roberto",
    booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.wmt-1.51",
    pages = "569--577",
}

How to Use

Prerequisites

• Python 3.9 or later

Installation

Clone the repository and install the required dependencies:

cd MaTESe
pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu116
pip install -e ./src

Download the checkpoints of the models and put them in the checkpoints directory

MaTESe (English only):

https://drive.google.com/file/d/12LmxaQP_s42RKORHeII97hJlNyUS2Pgg/view?usp=sharing

MaTESe (Supports English, German and Russian as target languages):

https://drive.google.com/file/d/1uajyhYYCu3qPfHNIU3RR2NXsvNIyYL4L/view?usp=sharing

MaTESe-QE (Supports the language pairs en-de, zh-en and en-ru):

https://drive.google.com/file/d/1ZFYTNroMijr9-vYyc1WL0DPnmyrJfwa_/view?usp=sharing

Usage

MaTESe can be used in several ways:

1. From the command line: You need to populate two out of the following three files: data/sources.txt, data/candidates.txt, and data/references.txt. Each line of these files must contain respectively a sentence in the source language, its candidate translation, and the corresponding reference translation (sources.txt is not needed if you are using MaTESe, references.txt is not needed if you are using MaTESe-QE).

   To run the evaluation using MaTESe, use the following command:

   python src/matese.py

   For the English-only version instead:

   python src/matese.py --metric matese-en

   And if you want to use MaTESe-QE:

   python src/matese.py --metric matese-qe

   These commands will create the files data/output.scores.txt and data/output.spans.txt with the result of the evaluation.

2. Interactively: If you prefer an interactive mode, you can use MaTESe with Streamlit:

   streamlit run src/demo.py

   This will start the Streamlit app. You can follow the instructions in the app to evaluate your translations.

3. Programmatically: In the following example you can see how it is possible to use MaTESe metrics in a Python program:

   from matese.metric import MaTESe
   
   candidates = ["This is a wrong translation in English"]
   references = ["This is a sentence in English"]
   
   metric = MaTESe.load_metric('matese-en') # pass 'matese' or 'matese-qe' for the other versions
   assessments = metric.evaluate(candidates, references=references)
   
   print(assessments[0])

   {'spans': [{'offset': (9, 27), 'error': 'Major'}], 'score': -5}
   

License

This work is under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.