Abstract The paper presents a novel unified algorithm for aligning sentences with their translations in bilingual data. With the help of ideas from a stack-based dynamic programming decoder for speech recognition (Ney 1984), the search is parametrized in a novel way such that the unified algorithm can be used on various types of data that have been previously handled by separate implementations: the extracted text chunk pairs can be either sub-sentential pairs, one-to-one, or many-to-many sentence-level pairs. The one-stage search algorithm is carried out in a single run over the data. Its memory requirements are independent of the length of the source document, and it is applicable to sentence-level parallel as well as comparable data. With the help of a unified beam-search candidate pruning, the algorithm is very efficient: it avoids any document-level pre-filtering and uses less restrictive sentence-level filtering. Results are presented on a Russian–English, a Spanish–English, and an Arabic–English extraction task. Based on simple word-based scoring features, text chunk pairs are extracted out of several trillion candidates, where the search is carried out on 300 processors in parallel.

This paper presents a novel online relevant set algorithm for a linearly scored block sequence translation model. The key component is a new procedure to directly optimize the global scoring function used by a statistical machine translation (SMT) decoder. This training procedure treats the decoder as a black-box, and thus can be used to optimize any decoding scheme. The novel algorithm is evaluated using different feature types: 1) commonly used probabilistic features, such as translation, language, or distortion model probabilities, and 2) binary features. In particular, encouraging results on a standard Arabic–English translation task are presented for a translation system that uses only binary feature functions. To further demonstrate the effectiveness of the novel training algorithm, a detailed comparison with the widely used minimum-error-rate (MER) training algorithm is presented using the same decoder and feature set. The online algorithm is simplified by introducing so-called “seed” block sequences which enable the training to be carried out without a gold standard block translation. While the online training algorithm is extremely fast, it also improves translation scores over the MER algorithm in some experiments.

This paper presents a novel training algorithm for a linearly-scored block sequence translation model. The key component is a new procedure to directly optimize the global scoring function used by a SMT decoder. No translation, language, or distortion model probabilities are used as in earlier work on SMT. Therefore our method, which employs less domain specific knowledge, is both simpler and more extensible than previous approaches. Moreover, the training procedure treats the decoder as a black-box, and thus can be used to optimize any decoding scheme. The training algorithm is evaluated on a standard Arabic-English translation task.

In this paper, we present a novel training method for a localized phrase-based prediction model for statistical machine translation (SMT). The model predicts blocks with orientation to handle local phrase re-ordering. We use a maximum likelihood criterion to train a log-linear block bigram model which uses real-valued features (e.g. a language model score) as well as binary features based on the block identities themselves, e.g. block bigram features. Our training algorithm can easily handle millions of features. The best system obtains a 18.6% improvement over the baseline on a standard Arabic-English translation task.

In this article, we describe an efficient beam search algorithm for statistical machine translation based on dynamic programming (DP). The search algorithm uses the translation model presented in Brown et al. (1993). Starting from a DP-based solution to the traveling-salesman problem, we present a novel technique to restrict the possible word reorderings between source and target language in order to achieve an efficient search algorithm. Word reordering restrictions especially useful for the translation direction German to English are presented. The restrictions are generalized, and a set of four parameters to control the word reordering is introduced, which then can easily be adopted to new translation directions. The beam search procedure has been successfully tested on the Verbmobil task (German to English, 8,000-word vocabulary) and on the Canadian Hansards task (French to English, 100,000-word vocabulary). For the medium-sized Verbmobil task, a sentence can be translated in a few seconds, only a small number of search errors occur, and there is no performance degradation as measured by the word error criterion used in this article.

We describe three approaches to statistical translation and present experimental results. The statistical translation approach uses two types of information: a translation model and a language model. The language model used is a bigram or general m-gram model. The translation model is decomposed into a lexical model and an alignment model. There are three approaches that are presented and tested in detail: the quasi-monotone alignment approach, the inverted alignment approach, and the alignment template approach. For each of these three approaches, a suitable search method is presented. The system has been tested on a limited-domain spoken-language task for which a bilingual corpus is available: the Verbmobil task (German-English, 7000-word vocabulary). We present experimental results for each of the three approaches. The experimental tests were performed on both the text transcription and the speech recognizer output.

This paper considers statistical parsing of Czech, which differs radically from English in at least two respects: (1) it is a highly inflected language, and (2) it has relatively free word order. These differences are likely to pose new problems for techniques that have been developed on English. We describe our experience in building on the parsing model of (Collins 97). Our final results- 80% dependency accuracy - represent good progress towards the 91% accuracy of the parser on English (Wall Street Journal) text.

In this paper, we describe a Dynamic Programming (DP) based search algorithm for statistical translation and present experimental results. The statistical translation uses two sources of information: a translation model and a language model. The language model used is a standard bigram model. For the translation model, the alignment probabilities are made dependent on the differences in the alignment positions rather than on the absolute positions. Thus, the approach amounts to a first-order Hidden Markov model (HMM) as they are used successfully in speech recognition for the time alignment problem. Under the assumption that the alignment is monotone with respect to the word order in both languages, an efficient search strategy for translation can be formulated. The details of the search algorithm are described. Experiments on the EuTrans corpus produced a word error rate of 5.1%.

In this paper, we describe a fast search algorithm for statistical translation based on dynamic programming (DP) and present experimental results.The approach is based on the assumption that the word alignment is monotone with respect to the word order in both languages.To reduce the search eort for this approach, we introduce two methods: an acceleration technique to eciently compute the dynamic programming recursion equation and a beam search strategy as used in speech recognition.The experimental tests carried out on the Verbmobil corpus showed that the search space, measured by the number of translation hypotheses, is reduced by a factor of about 230 without aecting the translation performance.