Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Savoldi, Beatrice"'
Machine translation (MT) models are known to suffer from gender bias, especially when translating into languages with extensive gendered morphology. Accordingly, they still fall short in using gender-inclusive language, also representative of non-bin
Externí odkaz:
http://arxiv.org/abs/2405.08477
Current automatic speech recognition (ASR) models are designed to be used across many languages and tasks without substantial changes. However, this broad language coverage hides performance gaps within languages, for example, across genders. Our stu
Externí odkaz:
http://arxiv.org/abs/2402.17954
Gender-neutral translation (GNT) that avoids biased and undue binary assumptions is a pivotal challenge for the creation of more inclusive translation technologies. Advancements for this task in Machine Translation (MT), however, are hindered by the
Externí odkaz:
http://arxiv.org/abs/2402.06041
As part of the WMT-2023 "Test suites" shared task, in this paper we summarize the results of two test suites evaluations: MuST-SHE-WMT23 and INES. By focusing on the en-de and de-en language pairs, we rely on these newly created test suites to invest
Externí odkaz:
http://arxiv.org/abs/2310.19345
Gender inequality is embedded in our communication practices and perpetuated in translation technologies. This becomes particularly apparent when translating into grammatical gender languages, where machine translation (MT) often defaults to masculin
Externí odkaz:
http://arxiv.org/abs/2310.05294
Autor:
Savoldi, Beatrice
Automatic translation tools have facilitated navigating multilingual contexts, by providing accessible shortcuts for gathering, processing, and spreading information. As language technologies become more widely used and deployed on a large scale, how
Externí odkaz:
https://hdl.handle.net/11572/380889
Publikováno v:
Hermes Journal of Language and Communication in Business no 63 2023
Machine Translation (MT) continues to make significant strides in quality and is increasingly adopted on a larger scale. Consequently, analyses have been redirected to more nuanced aspects, intricate phenomena, as well as potential risks that may ari
Externí odkaz:
http://arxiv.org/abs/2306.05882
Gender inclusivity in language technologies has become a prominent research topic. In this study, we explore gender-neutral translation (GNT) as a form of gender inclusivity and a goal to be achieved by machine translation (MT) models, which have bee
Externí odkaz:
http://arxiv.org/abs/2301.10075
Gender bias is largely recognized as a problematic phenomenon affecting language technologies, with recent studies underscoring that it might surface differently across languages. However, most of current evaluation practices adopt a word-level focus
Externí odkaz:
http://arxiv.org/abs/2203.09866
Having recognized gender bias as a major issue affecting current translation technologies, researchers have primarily attempted to mitigate it by working on the data front. However, whether algorithmic aspects concur to exacerbate unwanted outputs re
Externí odkaz:
http://arxiv.org/abs/2105.13782