sentiment) bilingual (in L1 and L2) parallel data U that are defined as follows.

where v E {1,2} denotes L1 or L2; the first term on the right-hand side is the likelihood of labeled data for both D1 and D2; and the second term is the likelihood of the unlabeled parallel data U.

However, there could be considerable noise in real-world parallel data , i.e.

We present a novel approach for joint bilingual sentiment classification at the sentence level that augments available labeled data in each language with unlabeled parallel data .

We also try to remove neutral sentences from the parallel data since they can introduce noise into our model, which deals only with positive and negative examples.

Co-Training with SVMs (Co-SVM): This method applies SVM-based co-training given both the labeled training data and the unlabeled parallel data following Wan (2009).

We furthermore find that improvements, albeit smaller, are obtained when the parallel data is replaced with a pseudo-parallel (i.e.

8 By making use of the unlabeled parallel data , our proposed approach improves the accuracy, compared to MaXEnt, by 8.12% (or 33.27% error reduction) on English and 3.44% (or 16.92% error reduction) on Chinese in the first setting, and by 5.07% (or 19.67% error reduction) on English and 3.87% (or 19.4% error reduction) on Chinese in the second setting.

Of course, for many language pairs and domains, parallel data is not available.

We now turn to the problem of MT without parallel data .

Next, we present two novel decipherment approaches for MT training without parallel data .

Bayesian Decipherment: We introduce a novel method for estimating IBM Model 3 parameters without parallel data , using Bayesian learning.

Before we tackle machine translation without parallel data , we first solve a simpler problem—word substitution decipherment.

thank Amamag Subramanya for helping us with the implementation of label propagation and Shankar Kumar for access to the parallel data .

The parallel data came from the Europarl corpus (Koehn, 2005) and the ODS United Nations dataset (UN, 2006).

Taking the intersection of languages in these resources, and selecting languages with large amounts of parallel data , yields the following set of eight Indo-European languages: Danish, Dutch, German, Greek, Italian, Portuguese, Spanish and Swedish.

0 Projection: Our third baseline incorporates bilingual information by projecting POS tags directly across alignments in the parallel data .

To bridge this gap, we consider a practically motivated scenario, in which we want to leverage existing resources from a resource-rich language (like English) when building tools for resource-poor foreign languages.1 We assume that absolutely no labeled training data is available for the foreign language of interest, but that we have access to parallel data with a resource-rich language.

We introduced a data collection framework that produces highly parallel data by asking different annotators to describe the same video segments.

While our data collection framework yields useful parallel data , it also has some limitations.

By pairing up descriptions of the same video in different languages, we obtain parallel data without requiring any bilingual skills.

We quantified the utility of our highly parallel data by computing the correlation between BLEU and human ratings when different numbers of references were available.