Machine translation using constraint-based synchronous grammar

Wong, Fai; Dong, Min; Hu, Dongfang

doi:10.1016/s1007-0214(06)70193-6

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…In the last few decades, a number of works have been done on Machine Translation (MT), the initial attempt was made in the 1950s (Booth, 1955). A number of approaches have been tried out by researchers, for example, rule-based MT (Poornima et al, 2011), hybrid-based MT (Salunkhe et al, 2016), and data-driven MT (Wong et al, 2006). All of these approaches have their own advantages and disadvantages.…”

Section: Related Workmentioning

confidence: 99%

NLPRL at WAT2019: Transformer-based Tamil – English Indic Task Neural Machine Translation System

Kumar¹,

Singh²

2019

Proceedings of the 6th Workshop on Asian Translation

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This paper describes the Machine Translation (MT) system submitted by the NLPRL team for the Tamil -English Indic Task at WAT 2019. We presented the Neural Machine Translation (NMT) system based on the Transformer approach. Training and performance of the model are evaluated on the En-Tam corpus (An English-Tamil Parallel Corpus) collected by researchers at UFAL (Institute of Formal and Applied Linguistics). The evaluation of the model done using Adequacy, BLEU, RIBES, and AM-FM scores, and the model improves translation in terms of Adequacy, RIBES and AM-FM as compared to the baseline.

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Section: Related Workmentioning

confidence: 99%

NLPRL at WAT2019: Transformer-based Tamil – English Indic Task Neural Machine Translation System

Kumar¹,

Singh²

2019

Proceedings of the 6th Workshop on Asian Translation

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“…Several works have been reported on machine translation (MT) in last a few decades, earliest one in 1950s (Booth, 1955). There are various approaches adopted by researchers such as rulebased MT (Ghosh et al, 2014;Wong et al, 2006), corpus-based MT (Wong et al, 2006), and hybridbased MT (Salunkhe et al, 2016). Each of these approaches has its own pros and cons.…”

Section: Literature Surveymentioning

confidence: 99%

Neural Machine Translation for English-Tamil

Choudhary¹,

Pathak²,

Saha³

et al. 2018

Proceedings of the Third Conference on Machine Translation: Shared Task Papers

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A huge amount of valuable resources is available on the web in English, which are often translated into local languages to facilitate knowledge sharing among local people who are not much familiar with English. However, translating such content manually is very tedious, costly, and time-consuming process. To this end, machine translation is an efficient approach to translate text without any human involvement. Neural machine translation (NMT) is one of the most recent and effective translation technique amongst all existing machine translation systems. In this paper, we apply NMT for English-Tamil language pair. We propose a novel neural machine translation technique using word-embedding along with Byte-Pair-Encoding (BPE) to develop an efficient translation system that overcomes the OOV (Out Of Vocabulary) problem for languages which do not have much translations available online. We use the BLEU score for evaluating the system performance. Experimental results confirm that our proposed MIDAS translator (8.33 BLEU score) outperforms Google translator (3.75 BLEU score).

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“…This section presents translations into RCG of three extensions of SDTG that have been presented in the literature, namely synchronous tree-adjoining grammar (STAG) (Shieber and Schabes 1990;Harbusch and Poller 1996), constraint-based synchronous grammar (CSG) (Wong et al 2006), and generalized multitext grammars (Melamed et al 2004), and study the classes of RCGs obtained from these translations.…”

Section: Beyond Syntax-directed Transduction Grammarsmentioning

confidence: 99%

“…STAG has also been argued to have applications in the context of semantic parsing (Nesson and Shieber 2007), where SDTG is probably too inexpressive, even if Wong and Mooney (2007), for instance, report some success. Wong et al (2006) propose that more complex synchronous grammars, i.e. CSGs, be put to use in MT.…”

Section: Beyond Syntax-directed Transduction Grammarsmentioning

confidence: 99%

A $${\mathcal{O}(|G|n^6)}$$ time extension of inversion transduction grammars

Søgaard

2011

Machine Translation

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Range concatenation grammars are viewed as a hierarchy of synchronous grammars. It is shown how inversion transduction grammars (ITGs) and extensions thereof, including synchronous tree-adjoining grammars, are captured by the hierarchy, and the expressivity and linguistic relevance of subclasses of the hierarchy are discussed. A O(|G|n 6 ) time extension of ITGs is proposed. The extension translates cross-serial dependencies into nested ones and handles complex kinds of discontinuous translation units and so-called inside-out alignments. In fact, our O(|G|n 6 ) time extension generates all possible alignments. It is shown that this additional expressivity comes at the cost of probabilistic parsing.

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Machine translation using constraint-based synchronous grammar

Cited by 10 publications

References 12 publications

NLPRL at WAT2019: Transformer-based Tamil – English Indic Task Neural Machine Translation System

NLPRL at WAT2019: Transformer-based Tamil – English Indic Task Neural Machine Translation System

Neural Machine Translation for English-Tamil

A $${\mathcal{O}(|G|n^6)}$$ time extension of inversion transduction grammars

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