Abstract | Evaluation experiments were conducted to calculate the correlation among human judgments, along with the scores produced using automatic evaluation methods for MT outputs obtained from the 12 machine translation systems in NTCIR—7. |
Experiments | These English output sentences are sentences that 12 machine translation systems in NTCIR—7 translated from 100 Japanese sentences. |
Experiments | Table 1 presents types of the 12 machine translation systems . |
Experiments | 12 machine translation systems in respective automatic evaluation methods, and “All” are the correlation coefficients using the scores of 1,200 output sentences obtained using the 12 machine translation systems . |
Introduction | High-quality automatic evaluation has become increasingly important as various machine translation systems have developed. |
Introduction | Evaluation experiments using MT outputs obtained by 12 machine translation systems in NTCIR—7(Fujii et al., 2008) demonstrate that the scores obtained using our system yield the highest correlation with the human judgments among the automatic evaluation methods in both sentence-level adequacy and fluency. |
Abstract | In this paper, we present a simple and effective method to address the issue of how to generate diversified translation systems from a single Statistical Machine Translation (SMT) engine for system combination. |
Abstract | First, a sequence of weak translation systems is generated from a baseline system in an iterative manner. |
Abstract | Then, a strong translation system is built from the ensemble of these weak translation systems . |
Background | Suppose that there are T available SMT systems {u1(/1*1), ..., uT(/1*T)}, the task of system combination is to build a new translation system v(u1(/l*1), mm?» from mm), mfg}. |
Introduction | With the emergence of various structurally different SMT systems, more and more studies are focused on combining multiple SMT systems for achieving higher translation accuracy rather than using a single translation system . |
Introduction | To reduce the burden of system development, it might be a nice way to combine a set of translation systems built from a single translation engine. |
Introduction | A key issue here is how to generate an ensemble of diversified translation systems from a single translation engine in a principled way. |
BabelNet | using (a) the human-generated translations provided in Wikipedia (the so-called inter-language links), as well as (b) a machine translation system to translate occurrences of the concepts within sense-tagged corpora, namely SemCor (Miller et al., 1993) — a corpus annotated with WordNet senses — and Wikipedia itself (Section 3.3). |
Conclusions | Further, we contribute a large set of sense occurrences harvested from Wikipedia and SemCor, a corpus that we input to a state-of-the-art machine translation system to fill in the gap between resource-rich languages — such as English — and resource-poorer ones. |
Experiment 2: Translation Evaluation | both from Wikipedia and the machine translation system . |
Methodology | Note that translations are sense-specific, as the context in which a term occurs is provided to the translation system . |
Methodology | An initial prototype used a statistical machine translation system based on Moses (Koehn et al., 2007) and trained on Europarl (Koehn, 2005). |
Abstract | Our model outperforms a GIZA++ Model-4 baseline by 6.3 points in F-measure, yielding a 1.1 BLEU score increase over a state-of-the-art syntax-based machine translation system . |
Experiments | For each set of translation rules, we train a machine translation system and decode a held-out test corpus for which we report results below. |
Experiments | We use a syntax-based translation system for these experiments. |
Introduction | Automatic word alignment is generally accepted as a first step in training any statistical machine translation system . |
Introduction | Generative alignment models like IBM Model-4 (Brown et al., 1993) have been in wide use for over 15 years, and while not perfect (see Figure 1), they are completely unsupervised, requiring no annotated training data to learn alignments that have powered many current state-of-the-art translation system . |
Alignment | First we obtain all models needed for a normal translations system . |
Alignment | The idea of forced alignment is to perform a phrase segmentation and alignment of each sentence pair of the training data using the full translation system as in decoding. |
Conclusion | In addition to the improved performance, the trained models are smaller leading to faster and smaller translation systems . |
Related Work | In (Liang et al., 2006) a discriminative translation system is described. |
Related Work | full and competitive translation system as starting point with reordering and all models included. |
Building the Corpus | In addition, the overall measure of success—induction of machine translation systems from limited resources—pushes the state of the art (Kumar et al., 2007). |
Conclusion | We need leaner methods for building machine translation systems ; new algorithms for cross-linguistic bootstrapping via multiple paths; more effective techniques for leveraging human effort in labeling data; scalable ways to get bilingual text for unwritten languages; and large scale social engineering to make it all happen quickly. |
Human Language Project | Another layer of the corpus consists of sentence and word alignments, required for training and evaluating machine translation systems , and for extracting bilingual lexicons. |
Abstract | We propose to incorporate two groups of linguistic features, which convey information from outside machine translation systems , into error detection: lexical and syntactic features. |
Conclusions and Future Work | Therefore our approach can be used for other machine translation systems , such as rule-based or example-based system, which generally do not produce N -best lists. |
SMT System | To obtain machine-generated translation hypotheses for our error detection, we use a state-of-the-art phrase-based machine translation system MOSES (Koehn et al., 2003; Koehn et al., 2007). |