2018 EMNLP EMNLP 2018

Findings of the WMT 2018 Shared Task on Parallel Corpus Filtering

Abstract

AbstractWe posed the shared task of assigning sentence-level quality scores for a very noisy corpus of sentence pairs crawled from the web, with the goal of sub-selecting 1% and 10% of high-quality data to be used to train machine translation systems. Seventeen participants from companies, national research labs, and universities participated in this task.

🧭 Keyword Pioneer — parallel corpus filtering
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Machine Learning, Natural Language Processing