SemSyn: Semantic-Syntactic Similarity Based Automatic Machine Translation Evaluation Metric
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
SemSyn: Semantic-Syntactic Similarity Based Automatic Machine Translation Evaluation Metric
Original language description
"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."
Czech name
—
Czech description
—
Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
—
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
"IETE Journal of Research"
ISSN
0377-2063
e-ISSN
—
Volume of the periodical
""
Issue of the periodical within the volume
2023
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
1-12
UT code for WoS article
000974191200001
EID of the result in the Scopus database
2-s2.0-85153234419