The coverage-contrastive-conditioning from szymonrucinski

Data and code for the paper "As Little as Possible, as Much as Necessary: Detecting Over- and Undertranslations with Contrastive Conditioning" (ACL 2022).

Detect potential coverage errors simply using NMT models (e.g. from the Hugging Face Hub)
A dataset of synthetic coverage errors in EN–DE and ZH–EN machine translations

Installation

Prerequisites: Python >= 3.8, PyTorch
pip install -r requirements.txt
Download dependency parsers for the languages used in the paper:

import stanza
stanza.download("en")
stanza.download("de")
stanza.download("zh-hans")

QE baseline (requires separate Python environment)

pip install -r baseline/requirements-baseline.txt

Usage Examples

Manual demonstration: Reproducing Figure 1

import math
from translation_models import load_translation_model

translation_model = load_translation_model("mbart50-one-to-many")

src = "Please exit the plane after landing."
tgt = "Bitte verlassen Sie das Flugzeug."

partial_sources = [
    "exit the plane after landing.",  # "Please" deleted
    "Please exit after landing.",  # "the plane" deleted
    "Please exit the plane.",  # "after landing" deleted
]

scores = translation_model.score(
    src_lang="en",
    tgt_lang="de",
    source_sentences=[src] + partial_sources,
    hypothesis_sentences=(4 * [tgt]),
)
print(list(map(math.exp, scores)))
# [0.3387762759789361, 0.14397865706857962, 0.20369576359159763, 0.7212898669689275]

Using the CoverageEvaluator class

from coverage.evaluator import CoverageEvaluator
from translation_models import load_forward_and_backward_model

forward_model, backward_model = load_forward_and_backward_model("mbart50", src_lang="en", tgt_lang="de")

evaluator = CoverageEvaluator(
  src_lang="en",
  tgt_lang="de",
  forward_evaluator=forward_model,
  backward_evaluator=backward_model,
)
result = evaluator.detect_errors(
  src="Please exit the plane after landing.",
  translation="Bitte verlassen Sie das Flugzeug.",
)
print(result)
# "Omission errors: after landing"

NMT models

This repo implements coverage error detection with the following NMT models:

mbart-large-50-one-to-many-mmt / mbart-large-50-many-to-one-mmt (used for the paper)
m2m100_418M
m2m100_1.2B

Custom NMT models can be added by subclassing translation_models.TranslationModel.

Reproducing the results

Segment-Level Comparison to Gold Data

Download data

Download the following files from https://github.com/google/wmt-mqm-human-evaluation:
- mqm_newstest2020_ende.tsv
- mqm_newstest2020_zhen.tsv
Place the files in data/mqm

Run contrastive conditioning

python -m evaluation.predict_mqm \
  --model-name mbart50 \
  --language-pair en-de \
  > predictions/out.jsonl

Evaluate contrastive conditioning

python -m evaluation.evaluate_mqm \
  --language-pair en-de \
  --split test \
  --predictions-path predictions/out.jsonl

Train the QE baseline

Activate the baseline Python environment

kiwi train baseline/config.en-de.yaml

Evaluate the baseline

python -m evaluation.evaluate_mqm_baseline \
  --language-pair en-de \
  --split test \
  --checkpoint-path <checkpoint_path> \
  --nmt-predictions-path predictions/out.jsonl

Synthetic Data

Download dataset

wget https://files.ifi.uzh.ch/cl/archiv/2022/mutamur/coverage_data.zip
unzip coverage_data.zip -d data/synthetic

Alternative: Generate new synthetic data

Prepare monolingual sentence lines (e.g. download English text from http://data.statmt.org/news-crawl/)

python -m data_generation.generate \
  --language-pair en-de \
  --src-path <file containing source sentence lines> \
  --translation-model-name mbart50-one-to-many

Run contrastive conditioning on synthetic data

python -m evaluation.predict_synthetic_testset \
  --model-name mbart50 \
  --language-pair en-de \
  --dataset-name data/synthetic/en-de.test

Run QE baseline on synthetic data

Activate the baseline Python environment

kiwi predict baseline/predict.en-de.yaml system.load=<checkpoint_path>

Evaluate both on synthetic test set

python -m evaluation.evaluate_on_synthetic_data \
  --nmt-predicted-source-tags-path predictions/en-de.test.mbart50.source_tags \
  --baseline-predicted-source-tags-labels-path <path> \
  --gold-source-tags-path data/synthetic/en-de.test.source_tags \
  --gold-target-tags-path data/synthetic/en-de.test.tags

License

Code: MIT License
Data: See READMEs in the respective subdirectories

Citation

@inproceedings{vamvas-sennrich-2022-little,
    title = "As Little as Possible, as Much as Necessary: Detecting Over- and Undertranslations with Contrastive Conditioning",
    author = "Vamvas, Jannis  and
      Sennrich, Rico",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-short.53",
    pages = "490--500",
}

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