The RWTH system for the IWSLT 2007 evaluation is a combination of several statistical machine translation systems. The combination includes Phrase-Based models, a n-gram translation model and a hierarchical phrase model. We describe the individual systems and the method that was used for combining the system outputs. Compared to our 2006 system, we newly introduce a hierarchical phrase-based tr...

In this paper we provide the largest published comparison of translation quality for phrase-based SMT and neural machine translation across 30 translation directions. For ten directions we also include hierarchical phrase-based MT. Experiments are performed for the recently published United Nations Parallel Corpus v1.0 and its large six-way sentencealigned subcorpus. In the second part of the p...

Compared to traditional statistical machine translation (SMT), neural machine translation (NMT) often sacrifices adequacy for the sake of fluency. We propose a method to combine the advantages of traditional SMT and NMT by exploiting an existing phrase-based SMT model to compute the phrase-based decoding cost for an NMT output and then using this cost to rerank the n-best NMT outputs. The main ...

We present two translation systems experimented for the shared-task of “Workshop on Statistical Machine Translation,” a phrase-based model and a hierarchical phrase-based model. The former uses a phrasal unit for translation, whereas the latter is conceptualized as a synchronousCFG in which phrases are hierarchically combined using non-terminals. Experiments showed that the hierarchical phraseb...

The problem of rare and unknown words is an important issue in Uyghur-Chinese machine translation, especially using neural machine translation model. We propose a novel way to deal with the rare and unknown words. Based on neural machine translation of using pointers over input sequence, our approach which consists of preprocess and post-process can be used in all neural machine translation mod...

Phrase-based translation models are widely studied in statistical machine translation (SMT). However, the existing phrase-based translation models either can not deal with non-contiguous phrases or reorder phrases only by the rules without an effective reordering model. In this paper, we propose a generalized reordering model (GREM) for phrase-based statistical machine translation, which is not...

Lexicalized reordering model plays a central role in phrase-based statistical machine translation systems. The reordering model specifies the orientation for each phrase and calculates its probability conditioned on the phrase. In this paper, we describe the necessity and the challenge of introducing such a reordering model for hierarchical phrase-based translation. To deal with the challenge, ...

This paper describes the work that we conducted for the Chinese-English (CE) task of the NTCIR-10 patent machine translation evaluation. We built standard phrase-based and hierarchical phrase-based statistical machine translation (SMT) systems with optimized word segmentation, adaptive language model and improved parameter tuning strategy. Our systems outperform official baselines by approximat...

This paper presents a phrase table implementation for theMoses system that computes phrase table entries for phrase-based statistical machine translation (PBSMT) on demand by sampling an indexed bitext. While this approach has been used for years in hierarchical phrase-based translation, the PBSMT community has been slow to adopt this paradigm, due to concerns that this would be slow and lead t...

This paper describes the submission of Johns Hopkins University for the shared translation task of ACL 2016 First Conference on Machine Translation (WMT 2016). We set up phrase-based, hierarchical phrase-based and syntax-based systems for all 12 language pairs of this year’s evaluation campaign. Novel research directions we investigated include: neural probabilistic language models, bilingual n...