38,845 research outputs found
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. Moreover, we use our
improved SMT system to initialize a dual NMT model, which is further fine-tuned
through on-the-fly back-translation. Together, we obtain large improvements
over the previous state-of-the-art in unsupervised machine translation. For
instance, we get 22.5 BLEU points in English-to-German WMT 2014, 5.5 points
more than the previous best unsupervised system, and 0.5 points more than the
(supervised) shared task winner back in 2014.Comment: ACL 201
Tuning syntactically enhanced word alignment for statistical machine translation
We introduce a syntactically enhanced word alignment model that is more flexible than state-of-the-art generative word
alignment models and can be tuned according to different end tasks. First of all, this model takes the advantages of
both unsupervised and supervised word alignment approaches by obtaining anchor alignments from unsupervised generative
models and seeding the anchor alignments into a supervised discriminative model. Second, this model offers the flexibility of tuning the alignment according to different
optimisation criteria. Our experiments show that using our word alignment in a Phrase-Based Statistical Machine Translation system yields a 5.38% relative increase
on IWSLT 2007 task in terms of BLEU score
Language Model Adaptation for Statistical Machine Translation with Structured Query Models
We explore unsupervised language model adaptation techniques for Statistical Machine Translation. The hypotheses from the machine translation output are converted into queries at different levels of representation power and used to extract similar sentences from very large monolingual text collection. Specific language models are then build from the retrieved data and interpolated with a general background model. Experiments show significant improvements when translating with these adapted language models
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