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    <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.</abstract>
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%0 Conference Proceedings
%T Controlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022
%A Vincent, Sebastian
%A Barrault, Loïc
%A Scarton, Carolina
%Y Salesky, Elizabeth
%Y Federico, Marcello
%Y Costa-jussà, Marta
%S Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland (in-person and online)
%F vincent-etal-2022-controlling
%X 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.
%R 10.18653/v1/2022.iwslt-1.31
%U https://aclanthology.org/2022.iwslt-1.31/
%U https://doi.org/10.18653/v1/2022.iwslt-1.31
%P 341-350

Markdown (Informal)

[Controlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022](https://aclanthology.org/2022.iwslt-1.31/) (Vincent et al., IWSLT 2022)

• Controlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022 (Vincent et al., IWSLT 2022)

ACL

• Sebastian Vincent, Loïc Barrault, and Carolina Scarton. 2022. Controlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022. In Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022), pages 341–350, Dublin, Ireland (in-person and online). Association for Computational Linguistics.