As a supplement to existing parallel training data, our automatically extracted parallel data yields substantial translation quality improvements in translating microblog text and modest improvements in translating edited news commentary.

Section 2 describes the related work in parallel data extraction.

Section 3 presents our model to extract parallel data within the same document.

We will now describe our method to extract parallel data from Microblogs.

these are also considered for the extraction of parallel data .

Prior work on finding parallel data attempts to reason about the probability that pairs of documents (x, y) are parallel.

,xn, and consisting of n tokens, and need to determine whether there is parallel data in X, and if so, where are the parallel segments and their languages.

The main problem we address is to find the parallel data when the boundaries of the parallel segments are not defined explicitly.

Automatic collection of parallel data is a well-studied problem.

We aim to propose a method that acquires large amounts of parallel data for free.

of language pairs, large amounts of parallel data

However, for most language pairs and domains there is little to no curated parallel data available.

Hence discovery of parallel data is an important first step for translation between most of the world’s languages.

of the full parallel text; we do not use the English side of the parallel data for actually building systems.

One disadvantage to the previous method for evaluating the SENSESPOTTING task is that it requires parallel data in a new domain.

Suppose we have no parallel data in the new domain at all, yet still want to attack the SENSESPOTTING task.

We operate under the framework of phrase sense disambiguation (Carpuat and Wu, 2007), in which we take automatically align parallel data in an old domain to generate an initial old-domain sense inventory.

Table 2: Basic characteristics of the parallel data .

In contrast, the SENSESPOTTING task consists of detecting when senses are unknown in parallel data .

From an applied perspective, the assumption of a small amount of parallel data in the new domain is reasonable: if we want an MT system for a new domain, we will likely have some data for system tuning and evaluation.

We presented a novel approach for inducing oov translations from a monolingual corpus on the source side and a parallel data using graph propagation.

From the deV and test sets, we extract all source words that do not appear in the phrase-table constructed from the parallel data .

Similarly, the value of original four probability features in the phrase-table for the new entries are set to l. The entire training pipeline is as follows: (i) a phrase table is constructed using parallel data as usual, (ii) oovs for dev and test sets are extracted, (iii) oovs are translated using graph propagation, (iv) oovs and translations are added to the phrase table, introducing a new feature type, (v) the new phrase table is tuned (with a LM) using MERT (Och, 2003) on the dev set.

The correctness of this gold standard is limited to the size of the parallel data used as well as the quality of the word alignment software toolkit, and is not 100% precise.

Given a (possibly small amount of) parallel data between the source and target languages, and a large monolingual data in the source language, we construct a graph over all phrase types in the monolingual text and the source side of the parallel corpus and connect phrases that have similar meanings (i.e.

When a relatively small parallel data is used, unlabeled nodes outnumber labeled ones and many of them lie on the paths between an oov node to labeled ones.

Increasing the size of the parallel data can reduce the number of oovs.

Pivot language techniques tackle this problem by taking advantage of available parallel data between the source language and a third language.

(2009) in which a graph is constructed from source language monolingual text1 and the source-side of the available parallel data .

Further, adapting large MSAflEnglish parallel data increases the lexical coverage, reduces OOVs to 0.7% and leads to an absolute BLEU improvement of 2.73 points.

adapted parallel data showed an improvement of 1.87 BLEU points over our best baseline.

Later, we applied an adaptation method to incorporate MSA/English parallel data .

— We built a phrasal Machine Translation (MT) system on adapted EgyptiarflEnglish parallel data , which outperformed a non-adapted baseline by 1.87 BLEU points.

— We used phrase-table merging (Nakov and Ng, 2009) to utilize MSA/English parallel data with the available in-domain parallel data .

This can be done by either translating between the related languages using word-level translation, character level transformations, and language specific rules (Durrani et al., 2010; Hajic et al., 2000; Nakov and Tiedemann, 2012), or by concatenating the parallel data for both languages (Nakov and Ng, 2009).

These translation methods generally require parallel data , for which hardly any exists between dialects and MSA.

Their best Egyptian/English system was trained on dialect/English parallel data .

The approaches in this third group often use parallel data to bridge the gap between languages.

However, they are very sensitive to the quality of parallel data , as well as the accuracy of a source-language model on it.

This approach yields an SRL model for a new language at a very low cost, effectively requiring only a source language model and parallel data .

It allows one to quickly construct an SRL model for a new language without manual annotation or language-specific heuristics, provided an accurate model is available for one of the related languages along with a certain amount of parallel data for the two languages.

notation projection approaches require sentence-and word-aligned parallel data and crucially depend on the accuracy of the syntactic parsing and SRL on the source side of the parallel corpus, cross-lingual model transfer can be performed using only a bilingual dictionary.

We use parallel data to construct a bilingual dictionary used in word mapping, as well as in the projection baseline.

The basic idea behind model transfer is similar to that of cross-lingual annotation projection, as we can see from the way parallel data is used in, for example, McDonald et al.

Our model can be extended for clustering any number of given languages together in a joint framework, and incorporate both monolingual and parallel data .

Monolingual Clustering: For every language pair, we train German word clusters on the monolingual German data from the parallel data .

Recall that A(cc, y) is the count of the alignment links between cc and 3/ observed in the parallel data , and A(cc) and A(y) are the respective marginal counts.

Since the objective consists of terms representing the entropy monolingual data (for each language) and parallel bilingual data, it is particularly attractive for the usual situation in which there is much more monolingual data available than parallel data .

This paper presents a novel method for inducing phrase-based translation units directly from parallel data , which we frame as learning an inverse transduction grammar (ITG) using a recursive Bayesian prior.

We have presented a novel method for leam-ing a phrase-based model of translation directly from parallel data which we have framed as leam-ing an inverse transduction grammar (ITG) using a recursive Bayesian prior.

Word-based translation models (Brown et al., 1993) remain central to phrase-based model training, where they are used to infer word-level alignments from sentence aligned parallel data , from

The sentences were drawn from the UN parallel data along with a variety of parallel news data from LDC and the GALE project.

If cross-lingual resources are available, such as parallel data , increased training data, better resources, or superior features can be used to improve the processing (ex.

They did so by training a bilingual model and then generating more training data from unlabeled parallel data .

We also report the first BLEU results on such a large-scale MT task under truly nonparallel settings (without using any parallel data or seed lexicon).

The results are encouraging and demonstrates the ability of the method to scale to large-scale settings while performing efficient inference with compleX models, which we believe will be especially useful for future MT application in scenarios where parallel data is hard to obtain.

But obtaining parallel data is an expensive process and not available for all language