Retrieval-augmented machine translation leverages examples from a translation memory by retrieving similar instances. These examples are used to condition the predictions of a neural decoder. We aim to improve the upstream retrieval step and consider a fixed downstream edit-based model: the multi-Levenshtein Transformer. The task consists of finding a set of examples that maximizes the overall coverage of the source sentence. To this end, we rely on the theory of submodular functions and explore new algorithms to optimize this coverage. We evaluate the resulting performance gains for the machine translation task.

The article aims at identifying what, from a structural point of view, AI based automatic translators cannot fully capture. It focuses on the machine's mistakes, in order to try to explain its causes. The biblical story of Ca\"in and Abel has been chosen because of its rich interpretive and critical tradition, but also because of its semantic difficulty. The investigation begins with the observation, for the translation of this text, of the language pairs and interfaces offered by the best known machine translation services (Google Translate, DeepL). A typology of the most frequent translation errors is then established. Finally, contemporary translations are compared, in order to underline the unique contribution of each. In conclusion, the article suggests a revision of translation theory and, corArtificial Intelligence, Translation, Limitations, Interpretation, Comparison, Unicityelatively, a reformulation of its technology concerning cultural texts.

As part of a larger project on optimal learning conditions in neural machine translation, we investigate characteristic training phases of translation engines. All our experiments are carried out using OpenNMT-Py: the pre-processing step is implemented using the Europarl training corpus and the INTERSECT corpus is used for validation. Longitudinal analyses of training phases suggest that the progression of translations is not always linear. Following the results of textometric explorations, we identify the importance of the phenomena related to chronological progression, in order to map different processes at work in neural machine translation (NMT).