Arabic-English Translation

This paper presents a maximum entropy ma- chine translation system using a minimal set of translation blocks (phrase-pairs). While recent phrase-based statistical machine trans- lation (SMT) systems achieve significant im- …...

Word lattice decoding has proven useful in spoken language translation; we argue that it provides a compelling model for translation of text genres, as well. We show that prior work …...

We introduce alignment models for Machine Trans- lation that take into account the context of a source word when determining its translation. Since the use of these contexts alone causes …...

Current statistical machine translation (SMT) systems are trained on sentence- aligned and word-aligned parallel text col- lected from various sources. Translation model parameters are estimated from the word alignments, and …...

In this paper, we present a method to em- ploy target-side syntactic contextual infor- mation in a Hierarchical Phrase-Based sys- tem. Our method uses Combinatory Cate- gorial Grammar (CCG) to …...

Hiero translation models have two lim- itations compared to phrase-based mod- els: 1) Limited hypothesis space; 2) No lexicalized reordering model. We pro- pose an extension of Hiero called Phrasal- …...

Recent advances in large-margin learning have shown that better generalization can be achieved by incorporating higher order information into the optimization, such as the spread of the data. However, these …...

Clustering words is a widely used technique in statistical natural language processing. It re- quires syntactic, semantic, and contextual fea- tures. Especially, semantic clustering is gain- ing a lot of …...

Neural networks have become the state- of-the-art approach for machine transla- tion (MT) in many languages. While linguistically-motivated tokenization tech- niques were shown to have significant ef- fects on the …...

AbstractWe present a multi-task learning framework for cross-lingual abstractive summarization to augment training data. Recent studies constructed pseudo cross-lingual abstractive summarization data to train their neural encoder-decoders. Meanwhile, we introduce …...