Paper Info
Title
BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translation.
Abstract
Mined bitexts can contain imperfect translations that yield unreliable training signals for Neural Machine Translation (NMT). While filtering such pairs out is known to improve final model quality, we argue that it is suboptimal in low-resource conditions where even mined data can be limited. In our work, we propose instead, to refine the mined bitexts via automatic editing: given a sentence in a language xf, and a possibly imperfect translation of it xe, our model generates a revised version xf' or xe' that yields a more equivalent translation pair (i.e., <xf, xe'> or <xf', xe>). We use a simple editing strategy by (1) mining potentially imperfect translations for each sentence in a given bitext, (2) learning a model to reconstruct the original translations and translate, in a multi-task fashion. Experiments demonstrate that our approach successfully improves the quality of CCMatrix mined bitext for 5 low-resource language-pairs and 10 translation directions by up to ~ 8 BLEU points, in most cases improving upon a competitive back-translation baseline.
Year
DOI
Venue
2022
10.18653/v1/2022.findings-naacl.110
The Annual Conference of the North American Chapter of the Association for Computational Linguistics
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Eleftheria Briakou100.34
Sida Wang254144.65
Luke S. Zettlemoyer33348163.34
Marjan Ghazvininejad49311.38