Parallel and comparable corpora are the main linguistic resources to identify multilingual terminology using automatic term extraction tools. However, parallel corpora are available only for certain languages, domains and genres, and comparable corpora have some limitations when identifying corresponding terms. To implement a more efficient selection of multilingual terminology, we compared the performance of using specialised parallel and comparable corpora applied to languages with various forms of capital in linguistic resources. This paper presents a comparative study in corpus linguistics in which we automatically identify terms in Catalan, Spanish and English in legislation and administrative law using parallel corpora, comparable corpora and a combined methodology based on both typologies of corpora together with word embeddings. We observe that the combined methodology implemented obtains a higher number of term candidates than when working exclusively with parallel or comparable corpora. The evaluation of the results is performed using a terminological thesaurus as a gold standard. The new methodology presented in our study permits us to identify multilingual terminology in an efficient way, especially in Catalan-Spanish languages.

While current NMT and GPT models improve fluency and context awareness, they struggle with creative texts, where figurative language and stylistic choices are crucial. Current evaluation methods fail to capture these nuances, which requires a more descriptive approach. We propose a taxonomy based on translation techniques to assess machine-generated translations more comprehensively. The pilot study we conducted comparing human machine-produced translations reveals that human translations employ a wider range of techniques, enhancing naturalness and cultural adaptation. NMT and GPT models, even with prompting, tend to simplify content and introduce accuracy errors. Our findings highlight the need for refined frameworks that consider stylistic and contextual accuracy, ultimately bridging the gap between human and machine translation performance.

This paper explores the fine-tuning and evaluation of neural machine translation (NMT) models for literary texts using RomCro v.2.0, an expanded multilingual and multidirectional parallel corpus. RomCro v.2.0 is based on RomCro v.1.0, but includes additional literary works, as well as texts in Catalan, making it a valuable resource for improving MT in underrepresented language pairs. Given the challenges of literary translation, where style, narrative voice, and cultural nuances must be preserved, fine-tuning on high-quality domain-specific data is essential for enhancing MT performance. We fine-tune existing NMT models with RomCro v.2.0 and evaluate their performance for six different language combinations using automatic metrics and for Spanish-Croatian and French-Catalan using manual evaluation. Results indicate that fine-tuned models outperform general-purpose systems, achieving greater fluency and stylistic coherence. These findings support the effectiveness of corpus-driven fine-tuning for literary translation and highlight the importance of curated high-quality corpus.

This paper illustrates the process of training and evaluating NMT systems for a language pair that includes a low-resource language variety.A parallel corpus of legal texts for Italian and South Tyrolean German has been compiled, with South Tyrolean German being the low-resourced language variety. As the size of the compiled corpus is insufficient for the training, we have combined the corpus with several parallel corpora using data weighting at sentence level. We then performed an evaluation of each combination and of two popular commercial systems.

In this paper, we describe the LitPC toolkit, a variety of tools and methods designed for the quick and effective creation of parallel corpora derived from literary works. This toolkit can be a useful resource due to the scarcity of curated parallel texts for this domain. We also feature a case study describing the creation of a Russian-English parallel corpus based on the literary works by Leo Tolstoy. Furthermore, an augmented version of this corpus is used to both train and assess neural machine translation systems specifically adapted to the author’s style.