The Important Influencing Factors in Machine Translation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AV774VZWA" target="_blank" >RIV/00216208:11320/23:V774VZWA - isvavai.cz</a>

Výsledek na webu

<a href="https://www.springerprofessional.de/en/the-important-influencing-factors-in-machine-translation/25456456" target="_blank" >https://www.springerprofessional.de/en/the-important-influencing-factors-in-machine-translation/25456456</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-15175-0_10" target="_blank" >10.1007/978-3-031-15175-0_10</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The Important Influencing Factors in Machine Translation

Popis výsledku v původním jazyce

"Factored-based machine translation deals with various important linguistics information of a word during the translation process. Morphologically rich languages like Hindi provide multiple word forms from the root or dictionary word by differing in morphological information such as part of speech (POS), affixes, number, gender, etc. So, linguistic factors can provide useful information while translating morphologically rich languages. Different factors contribute in a different manner. In this chapter, we study the significance of different linguistic factors in a phrase-based statistical machine translation (SMT) framework employed for Hindi to English translation. We performed experiments over HindEnCorp and ILCI dataset for Hindi–English. We find that POS+lemma+gender achieves the highest BLEU score (16.46) for HindEnCorp dataset, and POS+lemma+number achieves the highest BLEU score (19.11) for ILCI dataset."

Název v anglickém jazyce

The Important Influencing Factors in Machine Translation

Popis výsledku anglicky

"Factored-based machine translation deals with various important linguistics information of a word during the translation process. Morphologically rich languages like Hindi provide multiple word forms from the root or dictionary word by differing in morphological information such as part of speech (POS), affixes, number, gender, etc. So, linguistic factors can provide useful information while translating morphologically rich languages. Different factors contribute in a different manner. In this chapter, we study the significance of different linguistic factors in a phrase-based statistical machine translation (SMT) framework employed for Hindi to English translation. We performed experiments over HindEnCorp and ILCI dataset for Hindi–English. We find that POS+lemma+gender achieves the highest BLEU score (16.46) for HindEnCorp dataset, and POS+lemma+number achieves the highest BLEU score (19.11) for ILCI dataset."

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

"Machine Learning and Big Data Analytics"

ISBN

978-3-031-15175-0

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

119-126

Název nakladatele

Springer International Publishing

Místo vydání

Cham

Místo konání akce

Cham

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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