scholarly journals Parallel Treebanking Spanish-Quechua

2012 ◽  
Vol 7 ◽  
Author(s):  
Annette Rios ◽  
Anne Göhring ◽  
Martin Volk
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Word Segmentation ◽  
Word Alignment ◽  
Alignment Quality ◽  
Important Prerequisite ◽  
Bilingual Lexicon ◽  
Preliminary Work ◽  
First Impression ◽  
Agglutinative Language

Parallel treebanking is greatly facilitated by automatic word alignment. We work on building a trilingual treebank for German, Spanish and Quechua. We ran different alignment experiments on parallel Spanish-Quechua texts, measured the alignment quality, and compared these results to the figures we obtained aligning a comparable corpus of Spanish-German texts. This preliminary work has shown us the best word segmentation to use for the agglutinative language Quechua with respect to alignment. We also acquired a first impression about how well Quechua can be aligned to Spanish, an important prerequisite for bilingual lexicon extraction, parallel treebanking or statistical machine translation.

scholarly journals Evaluating a Pivot-Based Approach for Bilingual Lexicon Extraction

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Jae-Hoon Kim ◽  
Hong-Seok Kwon ◽  
Hyeong-Won Seo
Keyword(s):  
Machine Translation ◽  
Word Association ◽  
Statistical Machine Translation ◽  
Low Frequency ◽  
The Other ◽  
Word Alignment ◽  
Parallel Corpora ◽  
Empirical Results ◽  
Bilingual Lexicon ◽  
Resource Poor

A pivot-based approach for bilingual lexicon extraction is based on the similarity of context vectors represented by words in a pivot language like English. In this paper, in order to show validity and usability of the pivot-based approach, we evaluate the approach in company with two different methods for estimating context vectors: one estimates them from two parallel corpora based on word association between source words (resp., target words) and pivot words and the other estimates them from two parallel corpora based on word alignment tools for statistical machine translation. Empirical results on two language pairs (e.g., Korean-Spanish and Korean-French) have shown that the pivot-based approach is very promising for resource-poor languages and this approach observes its validity and usability. Furthermore, for words with low frequency, our method is also well performed.

Measuring Word Alignment Quality for Statistical Machine Translation

2007 ◽  
Vol 33 (3) ◽  
pp. 293-303 ◽  
Author(s):  
Alexander Fraser ◽  
Daniel Marcu
Keyword(s):  
Machine Translation ◽  
Recent Literature ◽  
Statistical Machine Translation ◽  
Critical Role ◽  
Word Alignment ◽  
Alignment Error ◽  
Alignment Quality ◽  
Alignment Task ◽  
State Of Affairs ◽  
The Relationship

Automatic word alignment plays a critical role in statistical machine translation. Unfortunately, the relationship between alignment quality and statistical machine translation performance has not been well understood. In the recent literature, the alignment task has frequently been decoupled from the translation task and assumptions have been made about measuring alignment quality for machine translation which, it turns out, are not justified. In particular, none of the tens of papers published over the last five years has shown that significant decreases in alignment error rate (AER) result in significant increases in translation performance. This paper explains this state of affairs and presents steps towards measuring alignment quality in a way which is predictive of statistical machine translation performance.

Does GIZA++ Make Search Errors?

2010 ◽  
Vol 36 (3) ◽  
pp. 295-302 ◽  
Author(s):  
Sujith Ravi ◽  
Kevin Knight
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Word Alignment ◽  
Alignment Algorithm

Word alignment is a critical procedure within statistical machine translation (SMT). Brown et al. (1993) have provided the most popular word alignment algorithm to date, one that has been implemented in the GIZA (Al-Onaizan et al., 1999) and GIZA++ (Och and Ney 2003) software and adopted by nearly every SMT project. In this article, we investigate whether this algorithm makes search errors when it computes Viterbi alignments, that is, whether it returns alignments that are sub-optimal according to a trained model.

scholarly journals End-to-end statistical machine translation with zero or small parallel texts

2016 ◽  
Vol 22 (4) ◽  
pp. 517-548 ◽  
Author(s):  
ANN IRVINE ◽  
CHRIS CALLISON-BURCH
Keyword(s):  
Detailed Analysis ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
Parallel Corpora ◽  
Low Resource ◽  
Bilingual Lexicon ◽  
Orthographic Similarity ◽  
Discriminative Model ◽  
End To End ◽  
Parallel Texts

AbstractWe use bilingual lexicon induction techniques, which learn translations from monolingual texts in two languages, to build an end-to-end statistical machine translation (SMT) system without the use of any bilingual sentence-aligned parallel corpora. We present detailed analysis of the accuracy of bilingual lexicon induction, and show how a discriminative model can be used to combine various signals of translation equivalence (like contextual similarity, temporal similarity, orthographic similarity and topic similarity). Our discriminative model produces higher accuracy translations than previous bilingual lexicon induction techniques. We reuse these signals of translation equivalence as features on a phrase-based SMT system. These monolingually estimated features enhance low resource SMT systems in addition to allowing end-to-end machine translation without parallel corpora.

The Critical Technology Development Status of Machine Translation

2013 ◽  
Vol 791-793 ◽  
pp. 1622-1625
Author(s):  
Dan Han ◽  
Zhi Han Yu
Keyword(s):  
Machine Translation ◽  
Technology Development ◽  
Statistical Machine Translation ◽  
Word Segmentation ◽  
Translation System ◽  
Chinese Word ◽  
Segmentation Method ◽  
Chinese Word Segmentation ◽  
Critical Technology ◽  
Translation Machine

In this article, we mainly introduce some basic concepts about machine translation. Machine translation means translating a natural language text to another by software. It can be divided into two categories: rule-based and corpus-based. IBM's statistical machine translation, Microsoft's multi-language machine translation project, AT & T's voice translation system and CMUs PANGLOSS system are three typical machine translation systems. Due to sentences are constructed by words continuously in Chinese. Chinese word segmentation is very essential. Three methods of Chinese word segmentation: segmentation methods based on string matching, segmentation method based on the understanding and segmentation method based on the statistics.

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