SemSyn: Semantic-Syntactic Similarity Based Automatic Machine Translation Evaluation Metric

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A6JBIV92E" target="_blank" >RIV/00216208:11320/23:6JBIV92E - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153234419&doi=10.1080%2f03772063.2023.2195819&partnerID=40&md5=f3bdce827d4a018759de4cfc6f5f1a05" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153234419&doi=10.1080%2f03772063.2023.2195819&partnerID=40&md5=f3bdce827d4a018759de4cfc6f5f1a05</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/03772063.2023.2195819" target="_blank" >10.1080/03772063.2023.2195819</a>

Jazyk výsledku

angličtina

Název v původním jazyce

SemSyn: Semantic-Syntactic Similarity Based Automatic Machine Translation Evaluation Metric

Popis výsledku v původním jazyce

"Machine translation evaluation is difficult and challenging for natural languages because different languages behave differently for the same dataset. Lexical-based metrics have been poorly represented semantic relationships and impose strict identity matching. However, translation and assessment become difficult for target morphologically rich languages with relatively free word order. Most of the standard evaluation metrics consider word order but do not effectively consider sentence structure. In this paper, we propose a novel machine translation evaluation metric SemSyn which incorporates both semantic and syntactic similarity. We incorporate the term frequency-inverse document frequency with the earth mover’s distance and word embedding to cover the semantic similarity. The part of speech and dependency parsing tags assist in covering syntactic similarity in the sentence structure. Part of speech and dependency parsing tags are extracted from universal dependencies and trained on the SpaCy library. Experimental results show that SemSyn has a higher correlation with human judgment than other evaluation metrics for morphologically rich language and other languages. © 2023 IETE."

Název v anglickém jazyce

SemSyn: Semantic-Syntactic Similarity Based Automatic Machine Translation Evaluation Metric

Popis výsledku anglicky

"Machine translation evaluation is difficult and challenging for natural languages because different languages behave differently for the same dataset. Lexical-based metrics have been poorly represented semantic relationships and impose strict identity matching. However, translation and assessment become difficult for target morphologically rich languages with relatively free word order. Most of the standard evaluation metrics consider word order but do not effectively consider sentence structure. In this paper, we propose a novel machine translation evaluation metric SemSyn which incorporates both semantic and syntactic similarity. We incorporate the term frequency-inverse document frequency with the earth mover’s distance and word embedding to cover the semantic similarity. The part of speech and dependency parsing tags assist in covering syntactic similarity in the sentence structure. Part of speech and dependency parsing tags are extracted from universal dependencies and trained on the SpaCy library. Experimental results show that SemSyn has a higher correlation with human judgment than other evaluation metrics for morphologically rich language and other languages. © 2023 IETE."

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

—

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

"IETE Journal of Research"

ISSN

0377-2063

e-ISSN

—

Svazek periodika

""

Číslo periodika v rámci svazku

2023

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

1-12

Kód UT WoS článku

000974191200001

EID výsledku v databázi Scopus

2-s2.0-85153234419