Controlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022
| Autor: | Vincent, Sebastian T., Barrault, Loïc, Scarton, Carolina |
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| Rok vydání: | 2022 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 8 pages, 10 figures, IWSLT22 camera-ready (system paper @ ACL-IWSLT Shared Task on Formality Control for Spoken Language Translation) |
| Databáze: | arXiv |
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