Unsupervised word alignment is most often modeled as a Markov process that generates a sentence f conditioned on its translation 6.

Extraction-based evaluations of alignment better coincide with the role of word aligners in machine translation systems (Ayan and Dorr, 2006).

Word alignment is the task of identifying corresponding words in sentence pairs.

The standard approach to word alignment employs directional Markov models that align the words of a sentence f to those of its translation 6, such as IBM Model 4 (Brown et al., 1993) or the HMM-based alignment rnodel(ngeletal,l996)

Our bidirectional model Q = (12,13) is a globally normalized, undirected graphical model of the word alignment for a fixed sentence pair (6, f Each vertex in the vertex set V corresponds to a model variable Vi, and each undirected edge in the edge set D corresponds to a pair of variables (W, Each vertex has an associated potential function w, that assigns a real-valued potential to each possible value v,- of 16.1 Likewise, each edge has an associated potential function gig-(vi, 213-) that scores pairs of values.

The highest probability word alignment vector under the model for a given sentence pair (6, f) can be computed exactly using the standard Viterbi algorithm for HMMs in O(|e|2 - time.

An alignment vector a can be converted trivially into a set of word alignment links A:

In addition, supervised word alignment models often use the output of directional unsupervised aligners as features or pruning signals.

A parallel idea that closely relates to our bidirectional model is posterior regularization, which has also been applied to the word alignment problem (Graca et al., 2008).

Another similar line of work applies belief propagation to factor graphs that enforce a one-to-one word alignment (Cromieres and Kurohashi, 2009).

We compare the accuracy of our proposed method of joint phrase alignment and extraction using the FLAT, HIER and HLEN models, with a baseline of using word alignments from GIZA++ and heuristic phrase extraction.

It should be noted that while Model 1 probabilities are used, they are only soft constraints, compared with the hard constraint of choosing a single word alignment used in most previous phrase extraction approaches.

Because of this, previous research has combined FLAT with heuristic phrase extraction, which exhaustively combines all adjacent phrases permitted by the word alignments (Och et al., 1999).

Figure l: A word alignment (a), and its derivations according to FLAT (b), and HIER (C).

However, as DeNero and Klein (2010) note, this two step approach results in word alignments that are not optimal for the final task of generating

As a solution to this, they proposed a supervised discriminative model that performs joint word alignment and phrase extraction, and found that joint estimation of word alignments and extraction sets improves both word alignment accuracy and translation results.

Figure 3: The phrase, block, and word alignments used in heuristic phrase extraction.

The traditional method for heuristic phrase extraction from word alignments exhaustively enumerates all phrases up to a certain length consistent with the alignment (Och et al., 1999).

We will call this heuristic extraction from word alignments HEUR-W.

Finally, we integrate the transliteration module into the GIZA++ word aligner and evaluate it on two word alignment tasks achieving improvements in both precision and recall measured against gold standard word alignments .

We evaluate our transliteration mining algorithm on three tasks: transliteration mining from Wikipedia InterLanguage Links, transliteration mining from parallel corpora, and word alignment using a word aligner with a transliteration component.

In the word alignment experiment, we integrate a transliteration module which is trained on the transliterations pairs extracted by our method into a word aligner and show a significant improvement.

We use the English/Hindi corpus from the shared task on word alignment , organized as part of the ACL 2005 Workshop on Building and Using Parallel Texts (WA05) (Martin et al., 2005).

Finally we integrate a transliteration module into the GIZA++ word aligner and show that it improves word alignment quality.

We evaluate our word alignment system on two language pairs using gold standard word alignments and achieve improvements of 10% and 13.5% in precision and 3.5% and 13.5% in recall.

Section 4 describes the evaluation of our mining method through both gold standard evaluation and through using it to improve word alignment quality.

1These numbers are language/corpus-dependent and are not necessarily to be taken as a general reflection of the overall quality of the word alignments for arbitrary language pairs.

Input: HPSG forest F5, target sentence T, word alignment A = j)}, target function word set {fw} appeared in T, and target chunk set {C}

However, forest-based translation systems, and, in general, most linguistically syntax-based SMT systems (Galley et al., 2004; Galley et al., 2006; Liu et al., 2006; Zhang et al., 2007; Mi et al., 2008; Liu et al., 2009; Chiang, 2010), are built upon word aligned parallel sentences and thus share a critical dependence on word alignments .

For example, even a single spurious word alignment can invalidate a large number of otherwise extractable rules, and unaligned words can result in an exponentially large set of extractable rules for the interpretation of these unaligned words (Galley et al., 2006).

What makes word alignment so fragile?

By dealing with the ambiguous word alignment instead of unaligned target words, syntax-based realignment models were proposed by (May

Specially, we observed that most incorrect or ambiguous word alignments are caused by function words rather than content words.

To establish a soft correspondence between the two languages, we use a second similarity function, which leverages standard unsupervised word alignment statistics (§3.3).3

3The word alignment methods do not use POS information.

To define a similarity function between the English and the foreign vertices, we rely on high-confidence word alignments .

Since our graph is built from a parallel corpus, we can use standard word alignment techniques to align the English sentences “De

All conditions use word alignments produced by sequential iterations of IBM model 1, HMM, and IBM model 4 in GIZA++, followed by “diag-and” symmetrization (Koehn et al., 2003).

We add inflection features for all words aligned to at least one English verb, adjective, noun, pronoun, or determiner, excepting definite and indefinite articles.

These features would be more properly defined based on the identity of the target word aligned to these quantifiers, but little ambiguity seems to arise from this substitution in practice.

These dependencies are inferred from source-side annotation Via word alignments , as depicted in figure 1, without any use of target-side dependency parses.