PICT@WAT 2022: Neural Machine Translation Systems for Indic Languages

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AER44PL9D" target="_blank" >RIV/00216208:11320/23:ER44PL9D - isvavai.cz</a>

Výsledek na webu

<a href="https://aclanthology.org/2022.wat-1.13" target="_blank" >https://aclanthology.org/2022.wat-1.13</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

PICT@WAT 2022: Neural Machine Translation Systems for Indic Languages

Popis výsledku v původním jazyce

"Translation entails more than simply translating words from one language to another. It is vitally essential for effective cross-cultural communication, thus making good translation systems an important requirement. We describe our systems in this paper, which were submitted to the WAT 2022 translation shared tasks. As part of the Multi-modal translation tasks' text-only translation sub-tasks, we submitted three Neural Machine Translation systems based on Transformer models for English to Malayalam, English to Bengali, and English to Hindi text translation. We found significant results on the leaderboard for English-Indic (en-xx) systems utilizing BLEU and RIBES scores as comparative metrics in our studies. For the respective translations of English to Malayalam, Bengali, and Hindi, we obtained BLEU scores of 19.50, 32.90, and 41.80 for the challenge subset and 30.60, 39.80, and 42.90 on the benchmark evaluation subset data."

Název v anglickém jazyce

PICT@WAT 2022: Neural Machine Translation Systems for Indic Languages

Popis výsledku anglicky

"Translation entails more than simply translating words from one language to another. It is vitally essential for effective cross-cultural communication, thus making good translation systems an important requirement. We describe our systems in this paper, which were submitted to the WAT 2022 translation shared tasks. As part of the Multi-modal translation tasks' text-only translation sub-tasks, we submitted three Neural Machine Translation systems based on Transformer models for English to Malayalam, English to Bengali, and English to Hindi text translation. We found significant results on the leaderboard for English-Indic (en-xx) systems utilizing BLEU and RIBES scores as comparative metrics in our studies. For the respective translations of English to Malayalam, Bengali, and Hindi, we obtained BLEU scores of 19.50, 32.90, and 41.80 for the challenge subset and 30.60, 39.80, and 42.90 on the benchmark evaluation subset data."

Druh

D - Stať ve sborníku

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

—

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

"Proceedings of the 9th Workshop on Asian Translation"

ISBN

—

ISSN

2951-2093

e-ISSN

—

Počet stran výsledku

5

Strana od-do

106-110

Název nakladatele

International Conference on Computational Linguistics

Místo vydání

Gyeongju, Republic of Korea

Místo konání akce

Gyeongju, Republic of Korea

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—