In this paper, we present a novel extension of a forest-to-string machine translation system with a reordering model.We predict reordering probabilities for every pair of source words with a model using features observed from the input parse forest.Our approach naturally deals with the ambiguity present in the input parse forest, but, at the same time, takes into account only the parts of the input forest used by the current translation hypothesis.The method provides improvement from 0.6 up to 1.0 point measured by (Ter -Bleu)/2 metric.

Machine translation benefits from system combination.We propose flexible interaction of hypergraphs as a novel technique combining different translation models within one decoder.We introduce features controlling the interactions between the two systems and explore three interaction schemes of hiero and forest-to-string models-specification, generalization, and interchange.The experiments are carried out on large training data with strong baselines utilizing rich sets of dense and sparse features.All three schemes significantly improve results of any single system on four testsets.We find that specification-a more constrained scheme that almost entirely uses forest-to-string rules, but optionally uses hiero rules for shorter spans-comes out as the strongest, yielding improvement up to 0.9 (Ter-Bleu)/2 points.We also provide a detailed experimental and qualitative analysis of the results.

In current statistical machine translation, IBM model based word alignment is widely used as a starting point to build phrase-based machine translation systems. However, such alignment model is separated from the rest of machine translation pipeline and optimized independently. Furthermore, structural information is not taken into account in the alignment model, which sometimes leads to incorrect alignments. In this paper, we present a novel method to connect a re-alignment model with a translation model in an integrated framework. We conduct bilingual chart parsing based on syntax-augmented synchronous context-free grammar. A Viterbi derivation tree is generated for each sentence pair with multiple features employed in a log-linear model. A new word alignment is created under the structural constraint from the Viterbi tree. Extensive experiments are conducted in a Farsi-to-English translation task in conversational speech domain and also a German-to-English translation task in text domain. Systems trained on the new alignment provide significant higher BLEU scores compared to a state-of-the-art baseline.

In this paper, we present advanced techniques that improved the performance of IBM Malay-English speech translation system significantly. During this work, we generated linguistics-driven hierarchical rules to enhance the formal syntax-based translation model; designed an active learning approach with bi-directional translations that outperformed unsupervised training; utilized translation direction information in parallel training corpus to build direction-specific interpolated language models for machine translation. There is 20% relative improvement achieved in the translation performance through all these techniques. A state-of-the-art Malay speech recognition system was also established as one of the crucial modules in the rapidly developed Malay-English speech translation.

Named Entity (NE) Translation is a challenging problem in Machine Translation (MT). Most of the training bi-text corpora for MT lack enough samples of NEs to cover the wide variety of contexts NEs can appear in. In this paper, we present a technique to translate NEs based on their NE types in addition to a phrase-based translation model. Our NE translation model is based on a syntax-based system similar to [1]; but we produce syntax-based rules with non-terminals as NE types instead of general non-terminals. Such classbased rules allow us to better generalize the context NEs. We show that our proposed method obtains an improvement of 0.66 BLEU score absolute as well as 0.26% in F1-measure over the baseline of phrase-based model in NE test set.

This demo presents CarDialer - an in-car cellphone control application. Its multi-modal user interface blends state-of-the-art speech recognition technology (including text-to-speech synthesis) with the existing well proven elements of a vehicle information system GUI (buttons mounted on a steering wheel and an LCD equipped with touch-screen). This conversational system provides access to name dialing, unconstrained dictation of numbers, adding new names, operations with lists of calls and messages, notification of presence, etc. The application is fully functional from the first start, no prerequisite steps such as configuration, speech recognition enrollment) are required. The presentation of the proposed multi-modal architecture goes beyond the specific application and presents a modular platform to integrate application logic with various incarnations of UI modalities.