2020/11/30 by Antonio Toral, Toral, Antonio, Antoni Oliver +3
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Software Engineering Research #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.2011.14979
Chapter published in the book Maschinelle Übersetzung für Übersetzungsprofis (pp. 276-295). Jörg Porsiel (Ed.), BDÜ Fachverlag, 2020. ISBN 978-3-946702-09-2
arxiv created 2020/11/30 · openalex publication_date 2020/11/30 · arxiv updated 2020/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this chapter we build a machine translation (MT) system tailored to the literary domain, specifically to novels, based on the state-of-the-art architecture in neural MT (NMT), the Transformer (Vaswani et al., 2017), for the translation direction English-to-Catalan. Subsequently, we assess to what extent such a system can be useful by evaluating its translations, by comparing this MT system against three other systems (two domain-specific systems under the recurrent and phrase-based paradigms and a popular generic on-line system) on three evaluations. The first evaluation is automatic and uses the most-widely used automatic evaluation metric, BLEU. The two remaining evaluations are manual and they assess, respectively, preference and amount of post-editing required to make the translation error-free. As expected, the domain-specific Transformer-based system outperformed the three other systems in all the three evaluations conducted, in all cases by a large margin.