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This paper presents the results of the WMT14 shared tasks, which included a standard news translation task, a separate medical translation task, a task for run-time estimation of machine translation quality, and a metrics task. This year, 143 machine translation systems from 23 institutions were submitted to the ten translation directions in the standard(More)
The CLEF-2006 Cross-Language Speech Retrieval (CL-SR) track included two tasks: to identify topically coherent segments of English interviews in a known-boundary condition, and to identify time stamps marking the beginning of topically relevant passages in Czech interviews in an unknown-boundary condition. Five teams participated in the English evaluation,(More)
Data preprocessing plays a crucial role in phrase-based statistical machine translation (PB-SMT). In this paper, we show how single-tokenization of two types of multi-word expressions (MWE), namely named entities (NE) and compound verbs, as well as their prior alignment can boost the performance of PB-SMT. Single-tokenization of compound verbs and named(More)
Current state-of-the-art Statistical Machine Translation systems are based on log-linear models that combine a set of feature functions to score translation hypotheses during decoding. The models are parametrized by a vector of weights usually optimized on a set of sentences and their reference translations, called development data. In this paper, we(More)
A spelling error detection and correction application is based on three main components: a dictionary (or reference word list), an error model and a language model. While most of the attention in the literature has been directed to the language model, we show how improvements in any of the three components can lead to significant cumulative improvements in(More)
We present an unsupervised approach to part-of-speech tagging based on projections of tags in a word-aligned bilingual parallel corpus. In contrast to the existing state-of-the-art approach of Das and Petrov, we have developed a substantially simpler method by automatically identifying " good " training sentences from the parallel corpus and applying(More)