Index of papers in Proc. ACL 2010 that mention
  • translation systems
Echizen-ya, Hiroshi and Araki, Kenji
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.
translation systems is mentioned in 11 sentences in this paper.
Topics mentioned in this paper:
Xiao, Tong and Zhu, Jingbo and Zhu, Muhua and Wang, Huizhen
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.
translation systems is mentioned in 17 sentences in this paper.
Topics mentioned in this paper:
Navigli, Roberto and Ponzetto, Simone Paolo
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).
translation systems is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Riesa, Jason and Marcu, Daniel
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 .
translation systems is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Wuebker, Joern and Mauser, Arne and Ney, Hermann
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.
translation systems is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Abney, Steven and Bird, Steven
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.
translation systems is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Xiong, Deyi and Zhang, Min and Li, Haizhou
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).
translation systems is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: