If a language is truly resource scarce, it is mostly unlikely to have an MT system .

Given that sentiment analysis is a less resource intensive task compared to machine translation, the use of an MT system is hard to justify for performing

For CLSA, clusters linked together using unlabelled parallel corpora do away with the need of translating labelled corpora from one language to another using an intermediary MT system or bilingual dictionary.

Further, this approach was found to be useful in cases where there are no MT systems to perform CLSA and the language of analysis is truly resource scarce.

Thus, wider implication of this study is that many widely spoken yet resource scare languages like Pashto, Sundanese, Hausa, Gujarati and Punjabi which do not have an MT system could now be analysed for sentiment.

A note on CLSA for truly resource scarce languages: Note that there is no publicly available MT system for English to Marathi.

However, many languages which are truly resource scarce, do not have an MT system or existing MT systems are not ripe to be used for CLSA (Balamurali et al., 2013).

No MT systems or bilingual dictionaries are used for this study.

Given the subtle and different ways the sentiment can be expressed which itself manifested as a result of cultural diversity amongst different languages, an MT system has to be of a superior quality to capture them.

In our quality estimation experiments we consider as metadata the MT system which produced the translation, and the identity of the source sentence being translated.

In this paper we model the task of predicting the quality of sentence translations using datasets that have been annotated by several judges with different levels of expertise and reliability, containing translations from a variety of MT systems and on a range of different types of sentences.

Partitioning the data by annotator (,uA) gives the best baseline result, while there is less information from the MT system or sentence identity.

The multitask learning methods performed best when using the annotator identity as the task descriptor, and less well for the MT system and sentence pair, where they only slightly improved over the baseline.

Examples of applications of QE include improving post-editing efficiency by filtering out low quality segments which would require more effort and time to correct than translating from scratch (Specia et al., 2009), selecting high quality segments to be published as they are, without post-editing (Soricut and Echihabi, 2010), selecting a translation from either an MT system or a translation memory for post-editing (He et al., 2010), selecting the best translation from multiple MT systems (Specia et al., 2010), and highlighting subsegments that need revision (Bach et al., 2011).

o It is often desirable to include alternative translations of source sentences produced by multiple MT systems , which requires multiple annotators for unbiased judgements, particularly for labels such as post-editing time (a translation seen a second time will require less editing effort).

It contains 299 English sentences translated into Spanish using two or more of eight MT systems randomly selected from all system submissions for WMT11 (Callison-Burch et al., 2011).

As our baseline, we apply a high-performing Chinese-English MT system (Zheng, 2008; Zheng et al., 2009) based on hierarchical phrase-based translation framework (Chiang, 2005).

For example, the baseline MT system mistakenly translated a person name “3% 21%?

For example, the following sentence: “§K%%Efifi§i$fi%§§ infill, EEWHWZ’Q (Gao Meimei’s strength really is formidable, I really admire her)” was mistakenly translated into “Gao the strength of the America and the America also really strong , ah , really admire her” by the baseline MT system because the person name “33%;: (Gaomeimei)” was mistakenly segmented into three words “$3 (Gao)”, “% (the America)” and “% (the America)”.

Furthermore, we calculated three Pearson product-moment correlation coefficients between human judgment scores and name-aware BLEU scores of these two MT systems .

A typical statistical MT system can only translate 60% person names correctly (Ji et al., 2009).

Two types of humble strategies were previously attempted to build name translation components which operate in tandem and loosely integrate into conventional statistical MT systems:

Preprocessing: identify names in the source texts and propose name translations to the MT system ; the name translation results can be simply but aggressively transferred from the source to the target side using word alignment, or added into phrase table in order to

Some statistical MT systems (e.g.

We show that the recovered empty categories not only improve the word alignment quality, but also lead to significant improvements in a large-scale state-of-the-art syntactic MT system .

In the Chinese-to-English MT experiments, we test two state-of-the-art MT systems .

The MT systems are optimized with pairwise ranking optimization (Hopkins and May, 2011) to maximize BLEU (Papineni et al., 2002).

We conducted some initial error analysis on our MT system output and found that most of the errors that are related to ECs are due to the missing *pro* and *PRO*.

For example, for a hierarchical MT system , some phrase pairs and Hiero (Chiang, 2005) rules can be extracted with recovered *pro* and *PRO* at the Chinese side.

In this work we also take advantages of the augmented Chinese parse trees (with ECs projected to the surface) and extract tree-to-string grammar (Liu et al., 2006) for a tree-to-string MT system .

We directly take advantage of the augmented parse trees in the tree-to-string grammar, which could have larger impact on the MT system performance.

Our results validate the hypothesis that it is possible to adapt an ordinary MT system into a working semantic parser.

Indeed, successful semantic parsers often resemble MT systems in several important respects, including the use of word alignment models as a starting point for rule extraction (Wong and Mooney, 2006; Kwiatkowski et al., 2010) and the use of automata such as tree transducers (Jones et al., 2012) to encode the relationship between NL and MRL.

Language modeling In addition to translation rules learned from a parallel corpus, MT systems also rely on an n-gram language model for the target language, estimated from a (typically larger) monolingual corpus.

UBL, like an MT system (and unlike most of the other systems discussed in this section), extracts rules at multiple levels of granularity by means of this splitting and unification procedure.

multilevel rules composed from smaller rules, a process similar to the one used for creating phrase tables in a phrase-based MT system .

In the results shown in Table 1 we observe that on English GeoQuery data, the hierarchical translation model achieves scores competitive with the state of the art, and in every language one of the MT systems achieves accuracy at least as good as a purpose-built semantic parser.

While differences in implementation and factors like programming language choice make a direct comparison of times necessarily imprecise, we note that the MT system takes less than three minutes to train on the GeoQuery corpus, while the publicly-available implementations of tsVB and UBL require roughly twenty minutes and five hours respectively on a 2.1 GHz CPU.

This was the first attempt at making proper quantitative and qualitative evaluation of the English—>Russian MT systems .

We have made the corpus comprising the source sentences, their human translations, translations by participating MT systems and the human evaluation data publicly available.8

We chose to retain the entire texts in the corpus rather than individual sentences, since some MT systems may use information beyond isolated sentences.

In our case the assessors were asked to make a pairwise comparison of two sentences translated by two different MT systems against a gold standard translation.

One of the main challenges in developing MT systems for Russian and for evaluating them is the need to deal with its free word order and complex morphology.

SGD is sensitive to the learning rate 77, which is difiicult to set in an MT system that mixes frequent “dense” features (like the language model) with sparse features (e. g., for translation rules).

We built Arabic-English and Chinese-English MT systems with Phrasal (Cer et al., 2010), a phrase-based system based on alignment templates (Och and Ney, 2004).

We introduce a new method for training feature-rich MT systems that is effective yet comparatively easy to implement.