An Effective Approach to Unsupervised Machine Translation

While machine translation has traditionally relied on large amounts of parallel corpora, a recent research line has managed to train both Neural Machine Translation (NMT) and Statistical Machine Translation (SMT) systems using monolingual corpora only. In this paper, we identify and address several deficiencies of existing unsupervised SMT approaches by exploiting subword information, developing a theoretically well founded unsupervised tuning method, and incorporating a joint refinement procedure... (read more)

PDF Abstract ACL 2019 PDF ACL 2019 Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Unsupervised Machine Translation WMT2014 English-French SMT + NMT (tuning and joint refinement) BLEU 36.2 # 2
Unsupervised Machine Translation WMT2014 English-German SMT + NMT (tuning and joint refinement) BLEU 22.5 # 1
Unsupervised Machine Translation WMT2014 French-English SMT + NMT (tuning and joint refinement) BLEU 33.5 # 3
Unsupervised Machine Translation WMT2014 German-English SMT + NMT (tuning and joint refinement) BLEU 27.0 # 1
Unsupervised Machine Translation WMT2016 English-German SMT + NMT (tuning and joint refinement) BLEU 26.9 # 3
Unsupervised Machine Translation WMT2016 German-English SMT + NMT (tuning and joint refinement) BLEU 34.4 # 3

Methods used in the Paper


METHOD TYPE
LINE
Graph Embeddings