In this paper we apply lightly-supervised training to a hierarchical phrase-based statistical machine translation system. We employ bitexts that have been built by automatically translating large amounts of monolingual data as additional parallel training corpora. We explore different ways of using this additional data to improve our system. Our results show that integrating a second translation model with only non-hierarchical phrases extracted from the automatically generated bitexts is a reasonable approach. The translation performance matches the result we achieve with a joint extraction on all training bitexts while the system is kept smaller due to a considerably lower overall number of phrases. 1

This paper describes the joint QUAERO submission to the WMT 2011 machine translation evaluation. Four groups (RWTH Aachen University, Karlsruhe Institute of Technology, LIMSI-CNRS, and SYSTRAN) of the QUAERO project submitted a joint translation for the WMT German→English task. Each group translated the data sets with their own systems. Then RWTH system combination combines these translations to a better one. In this paper, we describe the single systems of each group. Before we present the results of the system combination, we give a short description of the RWTH Aachen system combination approach. 1 Overview QUAERO is a European research and development program with the goal of developing multimedia and multilingual indexing and management tools for professional and general public applications

This paper describes the statistical machine translation (SMT) systems developed by RWTH Aachen University for the translation task of the EMNLP 2011 Sixth Workshop on

This paper describes Kriya — a new statistical machine translation (SMT) system that uses hierarchical phrases, which were first introduced in the Hiero machine translation system (Chiang, 2007). Kriya supports both a grammar extraction module for synchronous context-free grammars (SCFGs) and a CKY-based decoder. There are several re-implementations of Hiero in the machine translation community, but Kriya offers the following novel contributions: (a) Grammar extraction in Kriya supports extraction of the full set of Hiero-style SCFG rules but also supports the extraction of several types of compact rule sets which leads to faster decoding for different language pairs without compromising the BLEU scores. Kriya currently supports extraction of compact SCFGs such as grammars with one non-terminal and grammar pruning based on certain rule patterns, and (b) The Kriya decoder offers some unique improvements in the implementation of cube pruning, such as increasing diversity in the target language n-best output and novel methods for language model (LM) integration. The Kriya decoder can take advantage of parallelization using a networked cluster. Kriya supports KENLM and SRILM for language model queries and exploits n-gram history states in KENLM. This paper also provides several experimental results which demonstrate that the translation quality of Kriya compares favourably to the Moses (Koehn et al., 2007) phrase-based system in several language pairs while showing a substantial improvement for Chinese-English similar to Chiang (2007). We also quantify the model sizes for phrase-based and Hiero-style systems apart from presenting experiments comparing variants of Hiero models. 1.