While language style is considered to be automatic and relatively stable, its plasticity has not yet been studied in translations that require the translator to “step into the shoes of another person.” In the present study, we propose a psychological model of language adaptation in translations. Focusing on an established interindividual difference marker of language style, that is, gender, we examined whether translators assimilate to the original gendered style or implicitly project their own gendered language style. In a preregistered study, we investigated gender differences in language use in TED Talks ( N = 1,647) and their translations ( N = 544) in same- versus opposite-gender speaker/translator dyads. The results showed that translators assimilated to gendered language styles even when in mismatch to their own gender. This challenges predominating views on language style as fixed and fosters a more dynamic view of language style as also being shaped by social context.
Bridging Background Knowledge Gaps in Translation with Automatic Explicitation
Translations help people understand content written in another language. However, even correct literal translations do not fulfill that goal when people lack the necessary background to understand them. Professional translators incorporate explicitations to explain the missing context by considering cultural differences between source and target audiences. Despite its potential to help users, NLP research on explicitation is limited because of the dearth of adequate evaluation methods. This work introduces techniques for automatically generating explicitations, motivated by WikiExpl: a dataset that we collect from Wikipedia and annotate with human translators. The resulting explicitations are useful as they help answer questions more accurately in a multilingual question answering framework.
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- PAR ID:
- 10492723
- Publisher / Repository:
- Association for Computational Linguistics
- Date Published:
- Journal Name:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Page Range / eLocation ID:
- 9718 to 9735
- Format(s):
- Medium: X
- Location:
- Singapore
- Sponsoring Org:
- National Science Foundation
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