Personalized Machine Translation: Predicting Translational Preferences

By Shachar Mirkin, Jean-Luc Meunier. 2015

28 citations recorded by the source corpus.

Machine Translation (MT) has advanced in recent years to produce better translations for clients’ specific domains, and sophisticated tools allow professional translators to obtain translations according to their prior edits. We suggest that MT should be further personalized to the end-user level ‐ the receiver or the author of the text ‐ as done in other applications. As a step in that direction, we propose a method based on a recommender systems approach where the user’s preferred translation is predicted based on preferences of similar users. In our experiments, this method outperforms a set of non-personalized methods, suggesting that user preference information can be employed to provide better-suited translations for each user.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

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Citations in the corpus

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics (2021, background): “from informal to formal (Rao and Tetreault, 2018), personalization of machine translation systems to align with particular personal traits (Mirkin and Meunier, 2015), or persona-guided response generation of dialogue systems (Zhang et al.”
Motivating Personality-aware Machine Translation (2015, background): “In this work we investigate MT awareness of the author; in (Mirkin and Meunier, 2015) we address the task of readeraware MT.”
Personalized Machine Translation: Preserving Original Author Traits (2016, methodology): “Mirkin et al. (2015) motivated the need for personalization of MT models by showing that automatic translation does not preserve demographic and psychometric traits.”
Five sources of bias in natural language processing (2021, background): “Systems that explicitly model user demographics will help produce both more personalized and less biased translations (Font & Costa-jussà, 2019; Mirkin et al., 2015; Mirkin & Meuni-er, 2015; Saunders & Byrne, 2020; Stanovsky et al., 2019).”
The Importance of Modeling Social Factors of Language: Theory and Practice (2021, background): “…across speakers (Yang and Eisenstein, 2017), though attempts at creating personalized language technologies exist in information retrieval (Shen et al., 2005), recommender systems (Basilico and Hofmann, 2004), machine translation (Mirkin and Meunier, 2015), and language modeling (Federico, 1996).”
Case Study: Deontological Ethics in NLP (2020, background): “systems (Rabinovich et al., 2017; Michel and Neubig, 2018; Mirkin et al., 2015; Mirkin and Meunier, 2015) while preserving author attributes as well as controlling structural information like voice (Yamagishi et al.”
Can You Translate that into Man? Commercial Machine Translation Systems Include Stylistic Biases (2020, methodology): “Mirkin et al. (2015) have pointed out that demographic aspects of language do play a role in translation, and could help in personalization.”