MT on and for the Web

Boitet, Christian; Blanchon, Hervé; Seligman, Mark; Bellynck, Valérie

doi:10.1109/nlpke.2010.5587865

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…And because Google Translate typically uses English as a lingua franca to translate between other languages (e.g. Chinese → English → Portuguese) [16,4], our findings possibly extend to translations between gender neutral languages and non-gender neutral languages (apart from English) in general, although we have not tested this hypothesis.…”

Section: Discussionmentioning

confidence: 82%

Assessing gender bias in machine translation: a case study with Google Translate

Prates

Avelar

Lamb

2019

Neural Comput & Applic

217

109

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Recently there has been a growing concern in academia, industrial research labs and the mainstream commercial media about the phenomenon dubbed as machine bias, where trained statistical models -unbeknownst to their creators -grow to reflect controversial societal asymmetries, such as gender or racial bias. A significant number of Artificial Intelligence tools have recently been suggested to be harmfully biased towards some minority, with reports of racist criminal behavior predictors, Apple's Iphone X failing to differentiate between two distinct Asian people and the now infamous case of Google photos' mistakenly classifying black people as gorillas. Although a systematic study of such biases can be difficult, we believe that automated translation tools can be exploited through gender neutral languages to yield a window into the phenomenon of gender bias in AI.In this paper, we start with a comprehensive list of job positions from the U.S. Bureau of Labor Statistics (BLS) and used it in order to build sentences in constructions like "He/She is an Engineer" (where "Engineer" is replaced by the job position of interest) in 12 different gender neutral languages such as Hungarian, Chinese, Yoruba, and several others. We translate these sentences into English using the Google Translate API, and collect statistics about the frequency of female, male and gender-neutral pronouns in the translated output. We then show that Google Translate exhibits a strong tendency towards male defaults, in particular for fields typically associated to unbalanced gender distribution or stereotypes such as STEM (Science, Technology, Engineering and Mathematics) jobs. We ran these statistics against BLS' data for the frequency of female participation in each job position, in which we show that Google Translate fails to reproduce a real-world distribution of female workers. In summary, we provide experimental evidence that even if one does not expect in principle a 50:50 pronominal gender distribution, Google Translate yields male defaults much more frequently than what would be expected from demographic data alone.We believe that our study can shed further light on the phenomenon of machine bias and are hopeful that it will ignite a debate about the need to augment current statistical translation tools with debiasing techniques -which can already be found in the scientific literature.

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Section: Discussionmentioning

confidence: 82%

Assessing gender bias in machine translation: a case study with Google Translate

Prates

Avelar

Lamb

2019

Neural Comput & Applic

217

109

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“…By the end of 2008, Google released Google Translate tool. GT allows users to translate texts or web pages from one language to another automatically (Boitet, Blanchon, Seligman & Bellynck, 2009). In order to determine the most commonly used translations, Google uses statistical machine translation (Brown et al, 1990) based on linguistic modeling, statistical decision theory, and matching probability (Ney, 1995).…”

Section: Google Translatementioning

confidence: 99%

Saudi EFL Students’ Perceptions towards the Impact of Using Google Translate on Their Translation Skills

Alburaih,

Algraini

2024

ELT

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This study investigated the impact of using Google Translate (GT) on the translation skills of EFL both male and female students from different Saudi universities. The instrument of this study was a questionnaire that was modified according to the study’s purposes. The questionnaire contained 22 items divided into 3 sections. It was answered by 212 participants from different Saudi universities. According to the findings, participants had a positive attitude towards GT when using it as a translation tool for expanding their vocabulary. They were utilizing it as a quick and easy dictionary since they wanted to make sure of the meaning. Additionally, many participants mentioned that GT made them too lazy to check dictionaries. Students indicated that GT would negatively affect the text without reviewing. They confirmed that overreliance on GT can harm their translation abilities. Finally, students must know that GT is a double-edged weapon that can be both positive and detrimental if used incorrectly. According to the findings, recommendations and suggestions for further research were provided.

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“…Broadly applicable web data extraction software was developed to extract data from website sources [63,64]. Among the applications of this technology are the analysis of text-based documents, business and competitive intelligence [65], crawling of social web platforms [66,67], and bioinformatics [68].…”

Section: Case 2: Protein 3d Structure Prediction Dataset Extractionmentioning

confidence: 99%

“…Another barrier is the difficulty in processing large amounts of data from many websites and integrating them into the needed format. To resolve the issue of capturing huge, scattered, and unformatted structured and unstructured data, automated software was developed [64,67,[69][70][71]. However, to extract the relevant information from the internet or local data resources, it is necessary to understand the user's requirements and the semantics of the data.…”

Section: Case 2: Protein 3d Structure Prediction Dataset Extractionmentioning

confidence: 99%

Big Data Bot with a Special Reference to Bioinformatics

Al-Omari¹,

Tawalbeh²,

Akkam³

et al. 2023

Computers, Materials &Amp; Continua

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There are quintillions of data on deoxyribonucleic acid (DNA) and protein in publicly accessible data banks, and that number is expanding at an exponential rate. Many scientific fields, such as bioinformatics and drug discovery, rely on such data; nevertheless, gathering and extracting data from these resources is a tough undertaking. This data should go through several processes, including mining, data processing, analysis, and classification. This study proposes software that extracts data from big data repositories automatically and with the particular ability to repeat data extraction phases as many times as needed without human intervention. This software simulates the extraction of data from web-based (point-and-click) resources or graphical user interfaces that cannot be accessed using command-line tools. The software was evaluated by creating a novel database of 34 parameters for 1360 physicochemical properties of antimicrobial peptides (AMP) sequences (46240 hits) from various MARVIN software panels, which can be later utilized to develop novel AMPs. Furthermore, for machine learning research, the program was validated by extracting 10,000 protein tertiary structures from the Protein Data Bank. As a result, data collection from the web will become faster and less expensive, with no need for manual data extraction. The software is critical as a first step to preparing large datasets for subsequent stages of analysis, such as those using machine and deep-learning applications.

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MT on and for the Web

Cited by 4 publications

References 21 publications

Assessing gender bias in machine translation: a case study with Google Translate

Assessing gender bias in machine translation: a case study with Google Translate

Saudi EFL Students’ Perceptions towards the Impact of Using Google Translate on Their Translation Skills

Big Data Bot with a Special Reference to Bioinformatics

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