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- David Chiang, Yuval Marton, Philip Resnik
- EMNLP
- 2008
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)
- Djamé Seddah, Reut Tsarfaty, +20 authors Alina Wróblewska
- 2013
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)
- Yuval Marton, Philip Resnik
- ACL
- 2008
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)
- Yuval Marton, Chris Callison-Burch, Philip Resnik
- EMNLP
- 2009
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)
We describe the submission from the Columbia Arabic & Dialect Modeling group (CADIM) for the Shared Task at the Fourth Workshop on Statistical Parsing of Morphologically Rich Languages (SPMRL'2013). We participate in the Arabic Dependency parsing task for predicted POS tags and features. Our system is based on Marton et al. (2013).
- Yuval Marton, Ning Wu, Lisa Hellerstein
- ECIR
- 2005
Compression-based text classification methods are easy to apply, requiring virtually no preprocessing of the data. Most such methods are character-based, and thus have the potential to automatically capture non-word features of a document , such as punctuation, word-stems, and features spanning more than one word. However, compression-based classification… (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)
- Yuval Marton, Nizar Habash, Owen Rambow
- Computational Linguistics
- 2013
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)
This paper describes the techniques we explored to improve the translation of news text in the German-English and Hungarian-English tracks of the WMT09 shared translation task. Beginning with a convention hierarchical phrase-based system , we found benefits for using word seg-mentation lattices as input, explicit generation of beginning and end of sentence… (More)
- Yuval Marton, Saif Mohammad, Philip Resnik
- EMNLP
- 2009
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)