Index of papers in Proc. ACL 2010 that mention
  • translation model
Durrani, Nadir and Sajjad, Hassan and Fraser, Alexander and Schmid, Helmut
Introduction
Section 3 introduces two probabilistic models for integrating translations and transliterations into a translation model which are based on conditional and joint probability distributions.
Our Approach
Both of our models combine a character-based transliteration model with a word-based translation model .
Our Approach
Language Model for Unknown Words: Our model generates transliterations that can be known or unknown to the language model and the translation model .
Our Approach
We refer to the words known to the language model and to the translation model as LM-known and TM-known words respectively and to words that are unknown as LM-unknown and TM-unknown respectively.
Previous Work
Moreover, they are working with a large bitext so they can rely on their translation model and only need to transliterate NEs and OOVs.
Previous Work
Our translation model is based on data which is both sparse and noisy.
translation model is mentioned in 16 sentences in this paper.
Topics mentioned in this paper:
Chen, Boxing and Foster, George and Kuhn, Roland
Abstract
Similarity scores are used as additional features of the translation model to improve translation performance.
Experiments
The sense similarity scores are used as feature functions in the translation model .
Experiments
In particular, all the allowed bilingual corpora except the UN corpus and Hong Kong Hansard corpus have been used for estimating the translation model .
Experiments
The second one is the small data condition where only the FBIS3 corpus is used to train the translation model .
Hierarchical phrase-based MT system
The hierarchical phrase-based translation method (Chiang, 2005; Chiang, 2007) is a formal syntax-based translation modeling method; its translation model is a weighted synchronous context free grammar (SCFG).
Introduction
the translation probabilities in a translation model , for units from parallel corpora are mainly based on the co-occurrence counts of the two units.
translation model is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Wuebker, Joern and Mauser, Arne and Ney, Hermann
Alignment
, N is used for both the initialization of the translation model p(f|é) and the phrase model training.
Conclusion
We have shown that training phrase models can improve translation performance on a state-of-the-art phrase-based translation model .
Experimental Evaluation
The scaling factors of the translation models have been optimized for BLEU on the DEV data.
Experimental Evaluation
We will focus on the proposed leaving-one-out technique and show that it helps in finding good phrasal alignments on the training data that lead to improved translation models .
Introduction
Viterbi Word Alignment Phrase Alignment word translation models phrase translation models trained by EM Algorithm trained by EM Algorithm heuristic phrase phrase translation counts probabilities Phrase Translation Table ‘ ‘ Phrase Translation Table
Related Work
This is different from word-based translation models , where a typical assumption is that each target word corresponds to only one source word.
translation model is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Mi, Haitao and Liu, Qun
Abstract
We thus propose to combine the advantages of both, and present a novel constituency-to-dependency translation model , which uses constituency forests on the source side to direct the translation, and dependency trees on the target side (as a language model) to ensure grammaticality.
Conclusion and Future Work
In this paper, we presented a novel forest-based constituency-to-dependency translation model , which combines the advantages of both tree-to-string and string-to-tree systems, runs fast and guarantees grammaticality of the output.
Introduction
Linguistically syntax-based statistical machine translation models have made promising progress in recent years.
Model
Figure 1 shows a word-aligned source constituency forest FC and target dependency tree De, our constituency to dependency translation model can be formalized as:
Related Work
(2009), we apply forest into a new constituency tree to dependency tree translation model rather than constituency tree-to-tree model.
translation model is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Liu, Zhanyi and Wang, Haifeng and Wu, Hua and Li, Sheng
Improving Statistical Bilingual Word Alignment
IBM Model 1 only employs the word translation model to calculate the probabilities of alignments.
Improving Statistical Bilingual Word Alignment
In IBM Model 2, both the word translation model and position distribution model are used.
Improving Statistical Bilingual Word Alignment
IBM Model 3, 4 and 5 consider the fertility model in addition to the word translation model and position distribution model.
translation model is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Wu, Xianchao and Matsuzaki, Takuya and Tsujii, Jun'ichi
Conclusion
These tree-to-tree rules are applicable for forest-to-tree translation models (Liu et al., 2009a).
Experiments
4.1 Translation models
Experiments
In our translation models , we have made use of three kinds of translation rule sets which are trained separately.
translation model is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: