the weighted mixture of direct and pivot based phrase pairs to improve translation quality. 1

* This work was done when the first author was visiting Baidu. This paper proposes a novel approach that uti-lizes a machine learning method to improve pivot-based statistical machine translation (SMT). For language pairs with few bilingual data, a possible solution in pivot-based SMT using another

Recent research on multilingual statisti-cal machine translation focuses on the us-age of pivot languages in order to over-come resource limitations for certain lan-guage pairs. This paper provides new in-sights into what factors make a good pivot language and investigates the impact

This paper presents a novel approach to pivot-based machine translation (MT): while the state-of-the-art uses two statistical systems, this proposal treats the second system as a black box. Our approach effecively provides pivot-based MT to target languages for which no suitable bilingual corpora

Recent research on multilingual statistical machine translation (SMT) focuses on the usage of pivot languages in order to overcome resource limitations for certain language pairs. This paper proposes a new method to translate a dialect language into a foreign language by integrating transliteration

We present a framework for statistical machine translation of natural languages based on direct maximum entropy models, which contains the widely used source -channel approach as a special case. All knowledge sources are treated as feature functions, which depend on the source language

different collections. We present pivoted normalization, a technique that can be used to modify any normalization function thereby reducing the gap between the relevance and the retrieval probabilities. Training pivoted normalization on one collection, we can successfully use it on other (new) text

Pivot-based statistical machine translation (SMT) (Wu and Wang, 2007) has been shown a possible way of constructing a dictionary for the language pairs that have scarce parallel data (Tsunakawa et al.,

to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual

We compare two pivot strategies for phrase-based statistical machine translation (SMT), namely phrase translation and sentence translation. The phrase translation strategy means that we directly construct a phrase translation table (phrase-table) of the source and target language pair from two