In the contrast experiments, our DAE and HCDAE features are appended as extra features to the phrase table .

Following (Maskey and Zhou, 2012), we use the following 4 phrase features of each phrase pair (Koehn et al., 2003) in the phrase table as the first type of input features, bidirectional phrase translation probability (P (6| f) and P (f |e)), bidirectional lexical weighting (Lem(e|f) and Lex(f|e)),

Using the 4 original phrase features in the phrase table as the input features, they pre-trained the DBN by contrastive divergence (Hinton, 2002), and generated new unsupervised DBN features using forward computation.

These new features are appended as extra features to the phrase table for the translation decoder.

To speedup the pre-training, we subdivide the entire phrase pairs (with features X) in the phrase table into small mini-batches, each containing 100 cases, and update the weights after each mini-batch.

After the pre-training, for each phrase pair in the phrase table , we generate the DBN features (Maskey and Zhou, 2012) by passing the original phrase features X through the DBN using forward computation.

To determine an adequate number of epochs and to avoid over-fitting, we fine-tune on a fraction phrase table and test performance on the remaining validation phrase table, and then repeat fine-tuning on the entire phrase table for 100 epochs.

We evaluate our proposed method on two end-to-end SMT tasks ( phrase table pruning and decoding with phrasal semantic similarities) which need to measure semantic similarity between a source phrase and its translation candidates.

Two tasks are involved in the experiments: phrase table pruning that discards entries whose semantic similarity is very low and decoding with the phrasal semantic similarities as additional new features.

4.3 Phrase Table Pruning

Pruning most of the phrase table without much impact on translation quality is very important for translation especially in environments where memory and time constraints are imposed.

Accordingly, we evaluate the BRAE model on two end-to-end SMT tasks ( phrase table pruning and decoding with phrasal semantic similarities) which need to check whether a translation candidate and the source phrase are in the same meaning.

In phrase table pruning, we discard the phrasal translation rules with low semantic similarity.

The experiments show that up to 72% of the phrase table can be discarded without significant decrease on the translation quality, and in decoding with phrasal semantic similarities up to 1.7 BLEU score improvement over the state-of-the-art baseline can be achieved.

The baseline is a state-of-the-art phrase-based system; we perform word alignment using a lexicalized hidden Markov model, and then the phrase table is extracted using the grow—diag—final heuristic (Koehn et al., 2003).

The 13 baseline features (2 lexical, 2 phrasal, 5 HRM, and 1 language model, word penalty, phrase length feature and distortion penalty feature) were tuned using MERT (Och, 2003), which is also used to tune the 4 feature weights introduced by the secondary phrase table (2 lexical and 2 phrasal, other features being shared between the two tables).

Our goal is to obtain translation distributions for source phrases that are not present in the phrase table extracted from the parallel corpus.

If a source phrase is found in the baseline phrase table it is called a labeled phrase: its conditional empirical probability distribution over target phrases (estimated from the parallel data) is used as the label, and is sub-

Prior to generation, one phrase node for each target phrase occurring in the baseline phrase table is added to the target graph (black nodes in Fig.

The additional phrases are incorporated in the SMT system through a secondary phrase table (§2.5).

(2012) propose a method that utilizes a preexisting phrase table and a small bilingual lexicon, and performs BLI using monolingual corpora.

Decipherment-based approaches (Ravi and Knight, 2011; Dou and Knight, 2012) have generally taken a monolingual view to the problem and combine phrase tables through the log-linear model during feature weight training.

We define associations in cc and c primarily by looking up phrase pairs in a phrase table constructed using the PARALEX corpus (Fader et al., 2013).

We use the word alignments to construct a phrase table by applying the consistent phrase pair heuristic (Och and Ney, 2004) to all 5-grams.

This results in a phrase table with approximately 1.3 million phrase pairs.

This is sometimes referred to as a word graph (Ueffing et al., 2002), although in our case the segmented phrase table also produces tokens that correspond to morphemes.

(2010) address this problem by forcing the decoder’s phrase table to respect word boundaries, guaranteeing that each de-segmentable token sequence is local to an edge.

Alternatively, one can reparame-terize existing phrase tables as exponential models, so that translation probabilities account for source context and morphological features (Jeong et al., 2010; Subotin, 2011).