For rule extraction, we first identify initial phrase pairs on word-aligned sentence pairs by using the same criterion as most phrase-based translation models (Och and Ney, 2004) and Chiang’s HPB model (Chiang, 2005; Chiang, 2007).

We extract HD-HRs and NRRs based on initial phrase pairs , respectively.

We look for initial phrase pairs that contain other phrases and then replace sub-phrases with POS tags corresponding to their heads.

We divide the rules into three types: phrase rules, which only contain terminals and are the same as the phrase pairs in phrase-based system; monotone rules, which contain non-terminals and produce monotone translations; reordering rules, which also contain non-terminals but change the order of translations.

(2010) introduce topic model for filtering topic-mismatched phrase pairs .

Similarly, each phrase pair is also assigned with one specific topic.

A phrase pair will be discarded if its topic mismatches the document topic.

Lexical Weighting Lexical weighting features estimate the quality of a phrase pair by combining the lexical translation probabilities of the words in the phrase2 (Koehn et al., 2003).

Phrase pair probabilities p(€|7) are computed from these as described in Koehn et al.

For example, if topic 1:: is dominant in T, pk(é|7) may be quite large, but if p(k:|V) is very small, then we should steer away from this phrase pair and select a competing phrase pair which may have a lower probability in T, but which is more relevant to the test sentence at hand.

0 Baseline system translation output: 44.6% o Phrase pairs matching source n-grams: 67.8%

0 Extend a hypothesis with a new phrase pair 0 Recombine hypotheses with identical states

Och (1999) showed a method for inducing bilingual word classes that placed each phrase pair into a two-dimensional equivalence class.

To prevent overf1tting, the statistics of phrase pairs from a particular sentence was excluded from the phrase table when aligning that sentence.

A set of phrase pairs are extracted from word-aligned parallel corpus according to phrase extraction rules (Koehn et al., 2003).

We use the word translation table from IBM Model 1 (Brown et al., 1993) and compute the sum over all possible word alignments within a phrase pair without normalizing for length (Quirk et al., 2005).

Phrasal ITGs are ITGs that allow for non-terminals that can emit phrase pairs with multiple elements on both the source and target sides.

This probability is the combination of the generative probability of each phrase pair Pt(e:, f3) as well as the sum the probabilities over all shorter spans in straight and inverted order2

While the Bayesian phrasal ITG framework uses the previously mentioned phrase distribution Pt during search, it also allows for definition of a phrase pair prior probability Ppmofieg, f3), which can efficiently seed the search process with a bias towards phrase pairs that satisfy certain properties.

Whenever a phrase pair does not appear in a component phrase table, we set the corresponding pm(é| f) to a small epsilon value.

Whenever a phrase pair does not appear in a component phrase table, we set the corresponding pm(é|f) to 0; pairs in 15(6, that do not appear in at least one component table are discarded.

probability for each phrase pair (6, f) is given by: