This paper presents an overview of Statistical Machine Translation (SMT), which is currently
the dominant approach in Machine Translation (MT) research. In Way and Hearne (2011) …

In statistical machine translation, a researcher seeks to determine whether some innovation
(eg, a new feature, model, or inference algorithm) improves translation quality in comparison …

There has been a proliferation of recent work on SMT tuning algorithms capable of handling
larger feature sets than the traditional MERT approach. We analyze a number of these …

Sentiment analysis research has predominantly been on English texts. Thus there exist
many sentiment resources for English, but less so for other languages. Approaches to …

We present AROW, a new online learning algorithm that combines several properties of
successful: large margin training, confidence weighting, and the capacity to handle non …

Texts with potential educational value are becoming available through the Internet (eg,
Wikipedia, news services). However, using these new tests in classrooms introduces many …

We offer a simple, effective, and scalable method for statistical machine translation
parameter tuning based on the pairwise approach to ranking (Herbrich et al., 1999). Unlike …

When text is translated from one language into another, sentiment is preserved to varying
degrees. In this paper, we use Arabic social media posts as stand-in for source language …

We learn a joint model of sentence extraction and compression for multi-document
summarization. Our model scores candidate summaries according to a combined linear …

Abstract We use the Margin Infused Relaxed Algorithm of Crammer et al. to add a large
number of new features to two machine translation systems: the Hiero hierarchical …