Work

Findings of the WMT 2020 Shared Task on Parallel Corpus Filtering and Alignment

Year: 2020

Type: conference-paper

Abstract: Following two preceding WMT Shared Tasks on Parallel Corpus Filtering (Koehn et al., 2018(Koehn et al., , 2019)), we posed again the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting the highest-quality data to be used to train machine translation systems.This year, the task tackled the low resource condition of Pashto-English and Khmer-English and also included the challenge of sentence alignment from document pairs.10 participants from companies, national research labs, and universities participated in this task.

Source: Conference on Machine Translation (WMT)

DOI: 10.18653/v1/2020.wmt-1.78

Authors: Philipp Koehn, Vishrav Chaudhary, Ahmed El-Kishky, Naman Goyal, Peng-Jen Chen, Francisco Guzmán

Institutions: Johns Hopkins University, Meta (United States), University of Wolverhampton, Meta (Israel)

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W3119872155

FWCI: 3.83

Cites: 58

Cited by: 43

Related to: 20