IBM моделі 4

AbstractThe increasing interest in the statistical approach to Machine Translation is due to the development of effective algorithms for training the probabilistic models proposed so far. However, one of the …...

This paper describes a decoding algorithm for a syntax-based translation model (Ya- mada and Knight, 2001). The model has been extended to incorporate phrasal translations as presented here. In con- …...

We define noun phrase translation as a subtask of machine translation. This en- ables us to build a dedicated noun phrase translation subsystem that improves over the currently best general …...

∗ Traditional word alignment approaches cannot come up with satisfactory results for Named Entities. In this paper, we propose a novel approach using a maximum entropy model for named entity …...

Based upon a statistically trained speech translation system, in this study, we try to combine distinctive features derived from the two modules: speech recognition and statistical machine translation, in a …...

We present a discriminative, large- margin approach to feature-based matching for word alignment. In this framework, pairs of word tokens re- ceive a matching score, which is based on features …...

Most statistical translation systems are based on phrase translation pairs, or “blocks”, which are obtained mainly from word alignment. We use blocks to infer better word alignment and improved word …...

. Since subsentential alignment is critically important to the translation quality of an Example-Based Machine Translation (EBMT) system which operates by finding and combining phrase-level matches against the training examples, …...

AbstractAs an approach to syntax based statistical machine translation (SMT), Probabilistic Synchronous Dependency Insertion Grammars (PSDIG), introduced in (Ding and Palmer, 2005), are a version of synchronous grammars defined on …...

Recently, discriminative word alignment methods have achieved state-of-the-art accuracies by extend- ing the range of information sources that can be easily incorporated into aligners. The chief advan- tage of a …...

In this paper, we present a Bayesian Learn- ing based method to train word dependent transition models for HMM based word alignment. We present word alignment re- sults on the …...

A word in one language can be translated to zero, one, or several words in other languages. Using word fertility features has been shown to be useful in building word …...

We propose an unsupervised approach uti- lizing only raw corpora to enhance mor- phological alignment involving highly in- flected languages. Our method focuses on closed-class morphemes, modeling their influence on …...

We present a simple log-linear reparame- terization of IBM Model 2 that overcomes problems arising from Model 1’s strong assumptions and Model 2’s overparame- terization. Efficient inference, likelihood evaluation, and …...

Current word alignment models for statisti- cal machine translation do not address mor- phology beyond merely splitting words. We present a two-level alignment model that dis- tinguishes between words and …...

In this paper, we explore a novel bilin- gual word alignment approach based on DNN (Deep Neural Network), which has been proven to be very effective in var- ious machine …...

We propose a novel unsupervised word align- ment method that uses a constraint based on Inversion Transduction Grammar (ITG) parse trees to jointly unify two directional mod- els. Previous agreement …...