To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A9B8NHPWE" target="_blank" >RIV/00216208:11320/25:9B8NHPWE - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192858275&doi=10.1162%2ftacl_a_00645&partnerID=40&md5=7e6226f922bd41caf5031920bc0d908a" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192858275&doi=10.1162%2ftacl_a_00645&partnerID=40&md5=7e6226f922bd41caf5031920bc0d908a</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1162/tacl_a_00645" target="_blank" >10.1162/tacl_a_00645</a>
Alternative languages
Result language
angličtina
Original language name
To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translation
Original language description
We conduct a large-scale fine-grained comparative analysis of machine translations (MTs) against human translations (HTs) through the lens of morphosyntactic divergence. Across three language pairs and two types of divergence defined as the structural difference between the source and the target, MT is consistently more conservative than HT, with less morphosyntactic diversity, more convergent patterns, and more one-to-one alignments. Through analysis on different decoding algorithms, we attribute this discrepancy to the use of beam search that biases MT towards more convergent patterns. This bias is most amplified when the convergent pattern appears around 50% of the time in training data. Lastly, we show that for a majority of morphosyntactic divergences, their presence in HT is correlated with decreased MT performance, presenting a greater challenge for MT systems. © 2024 Association for Computational Linguistics.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Transactions of the Association for Computational Linguistics
ISSN
2307-387X
e-ISSN
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Volume of the periodical
12
Issue of the periodical within the volume
2024
Country of publishing house
US - UNITED STATES
Number of pages
17
Pages from-to
355-371
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85192858275