Statistical-based system combination approach to gain advantages over different machine translation systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10242519" target="_blank" >RIV/61989100:27240/19:10242519 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.cell.com/heliyon/fulltext/S2405-8440(19)36164-X#%20" target="_blank" >https://www.cell.com/heliyon/fulltext/S2405-8440(19)36164-X#%20</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.heliyon.2019.e02504" target="_blank" >10.1016/j.heliyon.2019.e02504</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Statistical-based system combination approach to gain advantages over different machine translation systems

Popis výsledku v původním jazyce

Every machine translation system has some advantages. We propose an improved statistical system combination approach to achieve the advantages of existing machine translation systems. The primary task is to score all the phrases of the outputs of different machine translation systems selected for combination. Three steps are involved in the proposed statistical system combination approach, viz., alignment, decoding, and scoring. Pair alignment is done in the first step to prevent duplication so that only a single phrase is chosen from various phrases containing the same information. Thus the alignment and scoring strategy are implemented in our approach. Hypotheses are built in the second step. In the third step, we calculate the scores for all the hypotheses. The hypothesis with the highest score is chosen as the final translated output. Wrong scoring can mislead to identify the best part from different systems. It may be noted that a particular phrase may appear in various ways in different translations. To resolve the challenges, we incorporate WordNet in the alignment phase and word2vec in the scoring phase along with the existing factors. We find that the system combination model using WordNet and word2vec injection improves the machine translation accuracy. In this work, we have merged three systems viz., Hierarchical machine translation system, Bing Microsoft Translate, and Google Translate. The broad tests of translation on eight language pairs with benchmark datasets demonstrate that the proposed system achieves better quality than the individual systems and the state-of-the-art system combination models. (C) 2019 The Authors

Název v anglickém jazyce

Statistical-based system combination approach to gain advantages over different machine translation systems

Popis výsledku anglicky

Every machine translation system has some advantages. We propose an improved statistical system combination approach to achieve the advantages of existing machine translation systems. The primary task is to score all the phrases of the outputs of different machine translation systems selected for combination. Three steps are involved in the proposed statistical system combination approach, viz., alignment, decoding, and scoring. Pair alignment is done in the first step to prevent duplication so that only a single phrase is chosen from various phrases containing the same information. Thus the alignment and scoring strategy are implemented in our approach. Hypotheses are built in the second step. In the third step, we calculate the scores for all the hypotheses. The hypothesis with the highest score is chosen as the final translated output. Wrong scoring can mislead to identify the best part from different systems. It may be noted that a particular phrase may appear in various ways in different translations. To resolve the challenges, we incorporate WordNet in the alignment phase and word2vec in the scoring phase along with the existing factors. We find that the system combination model using WordNet and word2vec injection improves the machine translation accuracy. In this work, we have merged three systems viz., Hierarchical machine translation system, Bing Microsoft Translate, and Google Translate. The broad tests of translation on eight language pairs with benchmark datasets demonstrate that the proposed system achieves better quality than the individual systems and the state-of-the-art system combination models. (C) 2019 The Authors

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Heliyon

ISSN

2405-8440

e-ISSN

—

Svazek periodika

5

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

—

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85072698660