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Minimum-error-rate training (MERT) is a bottleneck for current development in statistical machine translation because it is limited in the number of weights it can reliably optimize. Building on the work of Watanabe et al., we explore the use of the MIRA algorithm of Crammer et al. as an alternative to MERT. We first show that by parallel processing and(More)
This paper reports on the first shared task on statistical parsing of morphologically rich languages (MRLs). The task features data sets from nine languages, each available both in constituency and dependency annotation. We report on the preparation of the data sets, on the proposed parsing scenarios, and on the evaluation metrics for parsing MRLs given(More)
Untranslated words still constitute a major problem for Statistical Machine Translation (SMT), and current SMT systems are limited by the quantity of parallel training texts. Augmenting the training data with paraphrases generated by pivoting through other languages alleviates this problem, especially for the so-called " low density " languages. But(More)
In adding syntax to statistical MT, there is a tradeoff between taking advantage of linguistic analysis, versus allowing the model to exploit linguistically unmotivated mappings learned from parallel training data. A number of previous efforts have tackled this trade-off by starting with a commitment to linguistically motivated analyses and then finding(More)
We explore the contribution of different lexical and inflectional morphological features to dependency parsing of Arabic, a morphologically rich language. We experiment with all leading POS tagsets for Arabic, and introduce a few new sets. We show that training the parser using a simple regular expressive extension of an impoverished POS tagset with high(More)
We explore the contribution of lexical and inflectional morphology features to dependency parsing of Arabic, a morphologically rich language with complex agreement patterns. Using controlled experiments, we contrast the contribution of different part-of-speech (POS) tag sets and morphological features in two input conditions: machine-predicted condition (in(More)
We study challenges raised by the order of Arabic verbs and their subjects in statistical machine translation (SMT). We show that the boundaries of post-verbal subjects (VS) are hard to detect accurately, even with a state-of-the-art Arabic dependency parser. In addition, VS constructions have highly ambiguous reordering patterns when translated to English,(More)
Strictly corpus-based measures of semantic distance conflate co-occurrence information pertaining to the many possible senses of target words. We propose a corpus–thesaurus hybrid method that uses soft constraints to generate word-sense-aware distributional profiles (DPs) from coarser " concept DPs " (derived from a Roget-like thesaurus) and sense-unaware(More)