Experiments with Cross-Language Speech Retrieval for Lower-Resource Languages

Nair, Suraj; Ragni, Anton; Klejch, Ondřej; Galuščáková, Petra; Oard, Douglas W.

doi:10.1007/978-3-030-42835-8_13

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…The idea was that a document containing one possible translation candidate of a query term is more relevant than a document that contains multiple translations of that term. This probabilistic structured queries approach was also applied to Cross-Language Speech Retrieval (Nair et al, 2020). Darwish and Oard (2003) also exploited alternative translations of query terms.…”

Section: Related Workmentioning

confidence: 99%

Document Translation vs. Query Translation for Cross-Lingual Information Retrieval in the Medical Domain

Saleh¹,

Pecina²

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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We present a thorough comparison of two principal approaches to Cross-Lingual Information Retrieval: document translation (DT) and query translation (QT). Our experiments are conducted using the cross-lingual test collection produced within the CLEF eHealth information retrieval tasks in 2013-2015 containing English documents and queries in several European languages. We exploit the Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) paradigms and train several domain-specific and task-specific machine translation systems to translate the non-English queries into English (for the QT approach) and the English documents to all the query languages (for the DT approach). The results show that the quality of QT by SMT is sufficient enough to outperform the retrieval results of the DT approach for all the languages. NMT then further boosts translation quality and retrieval quality for both QT and DT for most languages, but still, QT provides generally better retrieval results than DT.

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Section: Related Workmentioning

confidence: 99%

Document Translation vs. Query Translation for Cross-Lingual Information Retrieval in the Medical Domain

Saleh¹,

Pecina²

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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“…For word-based confusion networks that are aligned to the one-best word boundary, equations ( 1) and ( 2) in section 2 are easily extended to incorporate an additional multiplicative factor for probabilities estimated from ASR confidence estimates. As Nair et al [123] found, this can yield better CLIR results than would be achieved from one-best ASR. Yarmohammadi et al [193] have also reported a beneficial effect from concatenating multiple recognition hypotheses from utterance-scale (i.e., sentence-like) confusion networks.…”

Section: Cross-language Speech Retrievalmentioning

confidence: 94%

Cross-language Information Retrieval

Galuščáková¹,

Oard²,

Nair³

2021

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Two key assumptions shape the usual view of ranked retrieval: (1) that the searcher can choose words for their query that might appear in the documents that they wish to see, and (2) that ranking retrieved documents will suffice because the searcher will be able to recognize those which they wished to find. When the documents to be searched are in a language not known by the searcher, neither assumption is true. In such cases, Cross-Language Information Retrieval (CLIR) is needed. This chapter reviews the state of the art for cross-language information retrieval and outlines some open research questions.

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