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- W4285309242 abstract "This paper describes the SLT-CDT-UoS group’s submission to the first Special Task on Formality Control for Spoken Language Translation, part of the IWSLT 2022 Evaluation Campaign. Our efforts were split between two fronts: data engineering and altering the objective function for best hypothesis selection. We used language-independent methods to extract formal and informal sentence pairs from the provided corpora; using English as a pivot language, we propagated formality annotations to languages treated as zero-shot in the task; we also further improved formality controlling with a hypothesis re-ranking approach. On the test sets for English-to-German and English-to-Spanish, we achieved an average accuracy of .935 within the constrained setting and .995 within unconstrained setting. In a zero-shot setting for English-to-Russian and English-to-Italian, we scored average accuracy of .590 for constrained setting and .659 for unconstrained." @default.
- W4285309242 created "2022-07-14" @default.
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- W4285309242 date "2022-01-01" @default.
- W4285309242 modified "2023-09-25" @default.
- W4285309242 title "Controlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022" @default.
- W4285309242 doi "https://doi.org/10.18653/v1/2022.iwslt-1.31" @default.
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