To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translation

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translation

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translation

Popis výsledku anglicky

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.

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

2024

Kód důvěrnosti údajů

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

Název periodika

Transactions of the Association for Computational Linguistics

ISSN

2307-387X

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

17

Strana od-do

355-371

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85192858275