Syntax augmented machine translation via chart parsing

Zollmann, Andreas; Venugopal, Ashish

doi:10.3115/1654650.1654671

Cited by 107 publications

(122 citation statements)

References 10 publications

(19 reference statements)

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“…Chiang's work uses labels similar to Zollmann and Venugopal (2006) with syntax on both sides. It applies Boolean features for rule-label and substituted-label combinations and uses discriminative training (MIRA: (Cherry and Foster 2012)) to learn which substitution combinations are associated with better translations.…”

Section: Soft Constraintsmentioning

confidence: 99%

“…SAMT We use the original label extraction scheme as described in Zollmann and Venugopal (2006). In particular we allow the binary "\", "/" and "+" operators.…”

Section: Experimental Structurementioning

confidence: 99%

“…We furthermore add the common rule application count, word penalty and rarity penalty features, 24 see e.g. Zollmann and Venugopal (2006) for details.…”

Section: Experimental Structurementioning

confidence: 99%

“…While the sequential approach considers the reordering process as a finite-state process over word or phrase positions, the hierarchical approach (Hiero) works with a synchronous context-free grammar (SCFG). For a decade now, the hierarchical approach (Chiang 2005) shows improved performance for language pairs with long-range reordering such as ChineseEnglish and Japanese-English (Chiang 2005;Zollmann and Venugopal 2006). The present work falls squarely within the hierarchical approach to reordering.…”

Section: Introductionmentioning

confidence: 99%

“…Generally speaking, the existing reordering approaches that are integrated within translation (i.e. during decoding) can be grouped into the sequential (Tillmann 2004;Galley and Manning 2008) and the hierarchical (Chiang 2005;Zollmann and Venugopal 2006). While the sequential approach considers the reordering process as a finite-state process over word or phrase positions, the hierarchical approach (Hiero) works with a synchronous context-free grammar (SCFG).…”

Section: Introductionmentioning

confidence: 99%

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Labeling hierarchical phrase-based models without linguistic resources

Wenniger

Sima’an

2015

Machine Translation

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Long-range word order differences are a well-known problem for machine translation. Unlike the standard phrase-based models which work with sequential and local phrase reordering, the hierarchical phrase-based model (Hiero) embeds the reordering of phrases within pairs of lexicalized context-free rules. This allows the model to handle long range reordering recursively. However, the Hiero grammar works with a single nonterminal label, which means that the rules are combined together into derivations independently and without reference to context outside the rules themselves. Follow-up work explored remedies involving nonterminal labels obtained from monolingual parsers and taggers. As of yet, no labeling mechanisms exist for the many languages for which there are no good quality parsers or taggers. In this paper we contribute a novel approach for acquiring reordering labels for Hiero grammars directly from the word-aligned parallel training corpus, without use of any taggers or parsers. The new labels represent types of alignment patterns in which a phrase pair is embedded within larger phrase pairs. In order to obtain alignment patterns that generalize well, we propose to decompose word alignments into trees over phrase pairs. Beside this labeling approach, we contribute coarse and sparse features for learning soft, weighted label-substitution as opposed to standard substitution. We report extensive experiments comparing our model to two baselines: Hiero and the known syntax augmented machine translation (SAMT) variant, which labels Hiero rules with nonterminals extracted from monolingual syntactic parses. We also test a simplified labeling scheme based on inversion transduction grammar (ITG English task we obtain performance improvement up to 1 BLEU point, whereas for the German-English task, where morphology is an issue, a minor (but statistically significant) improvement of 0.2 BLEU points is reported over SAMT. While ITG labeling does give a performance improvement, it remains sometimes suboptimal relative to our proposed labeling scheme.

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Section: Soft Constraintsmentioning

confidence: 99%

“…SAMT We use the original label extraction scheme as described in Zollmann and Venugopal (2006). In particular we allow the binary "\", "/" and "+" operators.…”

Section: Experimental Structurementioning

confidence: 99%

“…We furthermore add the common rule application count, word penalty and rarity penalty features, 24 see e.g. Zollmann and Venugopal (2006) for details.…”

Section: Experimental Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Labeling hierarchical phrase-based models without linguistic resources

Wenniger

Sima’an

2015

Machine Translation

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How much hybridization does machine translation Need?

Costa-jussà

2015

Asso for Info Science & Tech

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Rule-based and corpus-based machine translation (MT) have coexisted for more than 20 years. Recently, boundaries between the two paradigms have narrowed and hybrid approaches are gaining interest from both academia and businesses. However, since hybrid approaches involve the multidisciplinary interaction of linguists, computer scientists, engineers, and information specialists, understandably a number of issues exist.While statistical methods currently dominate research work in MT, most commercial MT systems are technically hybrid systems. The research community should investigate the benefits and questions surrounding the hybridization of MT systems more actively. This paper discusses various issues related to hybrid MT including its origins, architectures, achievements, and frustrations experienced in the community. It can be said that both rule-based and corpus-based MT systems have benefited from hybridization when effectively integrated. In fact, many of the current rule/corpus-based MT approaches are already hybridized since they do include statistics/rules at some point.

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