Controlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022

• URL: http://arxiv.org/abs/2205.05990v1
• Date: Thu, 12 May 2022 09:54:17 GMT
• Title: Controlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022
• Authors: Sebastian T. Vincent, Lo\"ic Barrault, Carolina Scarton
• Abstract summary: This paper describes the SLT-CDT-UoS group's submission to the first Special Task on Formality Control for Spoken Language Translation. Our efforts were split between two fronts: data engineering and altering the objective function for best hypothesis selection. 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.
• Score: 4.348327991071386
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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