[PDF] Top 20 Large Scale Decipherment for Out of Domain Machine Translation

... Theoretically, we can directly apply EM, as pro- posed in (Knight et al., 2006), or Bayesian decipher- ment (Ravi and Knight, 2011a) to solve the prob- lem. However, unlike letter substitution ciphers, word substitution ... See full document

... effectiveness of parameter sharing between the main encoder (or decoder) and the contextual en- coder. We observe an improvement of 5 percent- age points from s-hier-to-2 to s-hier-to-2.tied, and 4 percentage points from ... See full document

... but more interestingly, most German sentences now have a verb where the baseline had none. An- other profound effect can be observed for Rus- sian: the baseline almost invariably translated noun compounds incorrectly: ... See full document

... and machine translators that help to resolve acoustic or foreign language ambiguities by placing higher probability on more likely original underlying word ... See full document

... the decipherment problem for machine transla- ...simplified translation model as presented by Ravi and Knight ...this translation model allows insertions and dele- tions, hypotheses of ... See full document

... It is natural to think that creation of annotated data for training a POS tagger and a parser requires large amounts of efforts from annotators who understand the language well. However, we find that through the ... See full document

... Figure 2 shows a scatter plot of all of the PB- SMT experiments with word segmented Khmer, plotting BLEU score against Kendall’s tau dis- tance. The points show a strong correlation (co- efficient: 0.75). From this ... See full document

... This phenomenon is further evident in German when testing each model on Test2, which is se- lected from the bitext, and is thus closer matched to the larger tuning sets, but is separate from both the parallel data used ... See full document

... Neural Machine Translation (NMT), that represents a quantum leap in MT ...the translation process on the basis of a single, end-to-end probabilistic model (Luong et ...in translation quality, ... See full document

... for large vocabularies (>1 million words) us- ing such large training corpora (>30 billion to- ...statistical machine trans- lation system leads to improvements in trans- lation quality as ... See full document

... using domain-specific translation memories for training machine translation systems, we have shown that very limited amounts of in-domain data can be successfully complemented with ... See full document

... A common problem with automatic metrics for Machine Translation (MT) evaluation, such as BLEU (Papineni et al., 2002), is the need to have reference human translations. Also such metrics work best on a ... See full document

... Figure 3 shows the number of n-grams for lan- guage models trained on 13 million to 2 trillion to- kens. Both axes are on a logarithmic scale. The right scale shows the approximate size of the served ... See full document

... One of the most successful of all AL methods de- veloped to date is uncertainty sampling and it has been applied successfully many times (e.g.,(Lewis and Gale, 1994; Tong and Koller, 2002)). The intuition is clear: much ... See full document

... Neural machine translation (Bahdanau et ...neural machine translation, like most other large machine learning systems, requires large amounts of training examples sampled ... See full document

... Cube pruning (Chiang, 2007) is a widely used search strategy in state-of-the-art hierarchical de- coders. Some alternatives and extensions to the classical algorithm as proposed by David Chiang have been presented in the ... See full document

... Finally, we note that discriminative training methods often use a sentence level approximation to BLEU. It has been shown that optimizing corpus level BLEU versus sentence level BLEU can lead to improve- ments of up to ... See full document

... statistical machine translation ...In large and unstructured collections of documents such as the Web, however, metadata is often sparse or un- ...very large and diverse collections of ... See full document

... five translation tasks: Machine Translation of News, Machine Translation of IT domain, Biomedical Translation, Multimodal Machine Translation, and ... See full document

... Learning translation models from monolingual corpora could help ad- dress the challenges faced by modern-day MT sys- tems, especially for low resource language ...statistical machine trans- lation models ... See full document