Motivating Personality-aware Machine Translation

By Shachar Mirkin, Scott Nowson, Caroline De Brun, J. Perez. 2015

58 citations recorded by the source corpus.

Language use is known to be influenced by personality traits as well as by sociodemographic characteristics such as age or mother tongue. As a result, it is possible to automatically identify these traits of the author from her texts. It has recently been shown that knowledge of such dimensions can improve performance in NLP tasks such as topic and sentiment modeling. We posit that machine translation is another application that should be personalized. In order to motivate this, we explore whether translation preserves demographic and psychometric traits. We show that, largely, both translation of the source training data into the target language, and the target test data into the source language has a detrimental effect on the accuracy of predicting author traits. We argue that this supports the need for personal and personality-aware machine translation models.

Subfields: Artificial Intelligence

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Citations in the corpus

Getting Gender Right in Neural Machine Translation (2019, methodology): “When translating from one language into another, original author traits are partially lost, both in human and machine translations (Mirkin et al., 2015; Rabinovich et al., 2017).”
Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback (2017, background): “…question is whether we could extend our framework to model individual annotator preferences (Passonneau and Carpenter, 2014) or learn personalized models (Mirkin et al., 2015; Rabi-novich et al., 2017), and handle heteroscedastic noise (Park, 1966; Kersting et al., 2007; Antos et al., 2010).”
Extreme Adaptation for Personalized Neural Machine Translation (2018, background): “Recent work has incorporated side information about the author such as personality (Mirkin et al., 2015), gender (Rabinovich et al., 2017) or polite-ness (Sennrich et al., 2016a), but these methods can only handle phenomena where there are ex-Source plicit labels for the traits.”
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.”
Towards Cross-Lingual Generalization of Translation Gender Bias (2021, background): “its identification [17, 22, 26] and resolution [10, 23] have been dis-”
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).”
Attend to You: Personalized Image Captioning with Context Sequence Memory Networks (2017, background): “[18] enhance machine translation performance by exploiting personal traits.”
Predicting Authorship and Author Traits from Keystroke Dynamics (2018, background): “, 2018) or can be used for a wide range of applications like customer support, healthcare and personalized machine translation (Mirkin et al., 2015; Rabinovich et al., 2017).”
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.”
“You Sound Just Like Your Father” 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.”
Personalized Machine Translation: Predicting Translational Preferences (2015, background): “In (Mirkin et al., 2015) we investigate the first task, assessing whether the author’s demographic and personality traits are preserved over machine translation.”
Bleaching Text: Abstract Features for Cross-lingual Gender Prediction (2018, background): “It is of interest to several applications including personalized machine translation, forensics, and marketing (Mirkin et al., 2015; Rangel et al., 2015).”
RtGender: A Corpus for Studying Differential Responses to Gender (2018, background): “Gender labels were initially drawn from Mirkin et al. (2015) and the remaining labels were done manually.”
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.”