Two-Stage Refinement of Query Translation in a Pivot Language Approach to Cross-Lingual Information Retrieval: An Experiment at CLEF 2003

Kishida, Kazuaki; Kando, Noriko

doi:10.1007/978-3-540-30222-3_24

Cited by 14 publications

(12 citation statements)

References 6 publications

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“…For practical purposes, English is a world language, and there is good chance that translation resources exist between language X and English, and between English and language Y for transitive translation. In the CLEF environment (Cross Language Evaluation Forum: http://clef.iei.pi.cnr.it:2002), pivot bilingual retrieval has been investigated for European language pairs {X,Y} [Gollins and Sanderson 2001;Lehtokangas and Airio 2002;Kishida and Kando 2003]. English and many popular European languages like French and German are alphabetical and Latin-based, and can be considered close (e.g., cognates can be used).…”

Section: Introductionmentioning

confidence: 99%

Rich results from poor resources

Kwok

Choi

Dinstl

2005

ACM Transactions on Asian Language Information Processing

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We report on Korean monolingual, Chinese-Korean English-as-pivot bilingual, and Chinese-English bilingual CLIR experiments using MT software augmented with Web-based entity-oriented translation as resources in the NTCIR-4 environment. Simple stemming is helpful in improving bigram indexing for Korean retrieval. For word indexing, keeping nouns only is preferable. Web-based translation reduces untranslated terms left over after MT and substantially improves CLIR results. Translation concatenation is found to consistently improve CLIR effectiveness, while combining a retrieval list from bigram and word indexing is also helpful. A method to disambiguate multiple MT outputs using a log likelihood ratio threshold was tested. Depending on the nature of the title or description queries, bigram only or a retrieval combination, or relaxed or rigid evaluations, direct bilingual CLIR returned an average precision of 71-79% (English-Korean) and 76-84% (Chinese-English) of the corresponding Korean-Korean and English-English monolingual results. Using English as a pivot in Chinese-Korean CLIR provides about 55-65% the effectiveness that Korean alone does. Entity/terminology translation at the pivot language stage accounts for a large portion of this deficiency. A topic with comparatively worse Chinese-English bilingual result does not necessarily mean that it will continue to underperform (after further transitive Korean translation) at the Korean retrieval level.

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Section: Introductionmentioning

confidence: 99%

Rich results from poor resources

Kwok

Choi

Dinstl

2005

ACM Transactions on Asian Language Information Processing

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“…Wang et al (2006) induced alignment models by using two additional bilingual corpora to improve word alignment quality. Pivot language methods were also used for cross-language information retrieval (CLIR) (Gollins and Sanderson 2001;Kishida and Kando 2003), translation dictionary induction (Schafer and Yarowsky 2002), word-sense disambiguation (Diab and Resnik 2002), and so on.…”

Section: Related Workmentioning

confidence: 99%

“…For the first kind, pivot languages are employed to translate queries in cross-language information retrieval (CLIR) (Gollins and Sanderson, 2001;Kishida and Kando, 2003). These methods only used the available dictionaries to perform word by word translation.…”

Section: Related Workmentioning

confidence: 99%

Pivot language approach for phrase-based statistical machine translation

Wu¹,

Wang²

2007

Machine Translation

137

131

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This paper proposes a novel method for phrase-based statistical machine translation based on the use of a pivot language. To translate between languages L s and L t with limited bilingual resources, we bring in a third language, L p , called the pivot language. For the language pairs L s − L p and L p − L t , there exist large bilingual corpora. Using only L s − L p and L p − L t bilingual corpora, we can build a translation model for L s − L t . The advantage of this method lies in the fact that we can perform translation between L s and L t even if there is no bilingual corpus available for this language pair. Using BLEU as a metric, our pivot language approach significantly outperforms the standard model trained on a small bilingual corpus. Moreover, with a small L s − L t bilingual corpus available, our method can further improve translation quality by using the additional L s − L p and L p − L t bilingual corpora.

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“…The remaining terms, el, e3, e4, would be removed as potentially erroneous translations. More recent results with the use of a pivot language are found in [Kishida and Kando, 2003]. …”

Section: 245mentioning

confidence: 99%