Sexism Prediction in Spanish and English Tweets Using Monolingual and Multilingual BERT and Ensemble Models

Paula, Angel Felipe Magnossão de; Silva, Roberto Fray da; Schlicht, Ipek Baris

doi:10.48550/arxiv.2111.04551

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2022

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“…As already mentioned, this work is restricted only to job titles in English. Another direction of further investigations can be naturally focused on language agnostic as well as multilingual approaches (analogous to [16] for instance) which will be able to detect uninformative titles also in other languages.…”

Section: Further Workmentioning

confidence: 99%

Detecting Uninformative Research Job Titles via Classifier Failures—Zero Shot Approach

Víta¹

2022

Annals of Computer Science and Information Systems

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The aim of this paper is to introduce a novel approach to detecting "uninformative" job titles in research domain, i.e., detecting titles that convey little or no information about the focus and/or content of a particular job -like "Academic staff member AP/2", "PhD student position" etc. Such job titles decrease the success rate of job advertisements. The proposed approach belongs to zero shot approaches -it exploits only existing, easy accessible classification of jobs to research fields and it does not require any additional (manual) annotations. This work introduces an experimental corpus and provides preliminary results of our approach.

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Section: Further Workmentioning

confidence: 99%

Detecting Uninformative Research Job Titles via Classifier Failures—Zero Shot Approach

Víta¹

2022

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

Sexism Prediction in Spanish and English Tweets Using Monolingual and Multilingual BERT and Ensemble Models

Cited by 1 publication

References 0 publications

Detecting Uninformative Research Job Titles via Classifier Failures—Zero Shot Approach

Detecting Uninformative Research Job Titles via Classifier Failures—Zero Shot Approach

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