Sentiment induced phrase-based machine translation: Robustness analysis of PBSMT with senti-module

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A4GGWHRMZ" target="_blank" >RIV/00216208:11320/23:4GGWHRMZ - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0952197623011612" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0952197623011612</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.engappai.2023.106977" target="_blank" >10.1016/j.engappai.2023.106977</a>

Result language

angličtina

Original language name

Sentiment induced phrase-based machine translation: Robustness analysis of PBSMT with senti-module

Original language description

"Every type of machine translation system (i.e. neural, statistical, rule-based machine translation system) is equal important to build a sophistical hybrid machine translation system. Keeping this fact in my mind, I concentrate to improve statistical machine translation system with more natural way. In this paper, I try to preserve sentiment after translation to improve the overall accuracy of the machine translation system. So, I introduced senti-model here. A senti-model (sentiment model), translation model, language model, and distortion model are incorporated on the top of the beam search algorithm for decoding. At first, sentiment information is learned and modeled with translation probability by using this algorithm. Thereafter, I decode the source sentences-based on the contextual information. Overall procedure of translation modeling with a sentiment, parameter estimation for it, and senti-translation decoding (decoding with the sentiment model) are presented with empirical evidence. Experiments on a benchmark English–Hindi dataset shows that the proposed model is capable to improve the accuracy (in terms of 4.66 BLEU points, 4.09 LeBleu points, 4.67 NIST points, 5.71 RIBES points) significantly and preserves sentiment 7.79% more than the state-of-the-art technique."

Czech name

—

Czech description

—

Type

J_ost - Miscellaneous article in a specialist periodical

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

—

Publication year

2023

Confidentiality

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

Name of the periodical

"Engineering Applications of Artificial Intelligence"

ISSN

0952-1976

e-ISSN

—

Volume of the periodical

126

Issue of the periodical within the volume

2023-11-1

Country of publishing house

US - UNITED STATES

Number of pages

11

Pages from-to

1-11

UT code for WoS article

—

EID of the result in the Scopus database

—