Abstract
Linguistik International 2020 Although emotions are universal concepts, transferring the different shades
of emotion from one language to another may not always be straightforward for
human translators, let alone for machine translation systems. Moreover, the
cognitive states are established by verbal explanations of experience which is
shaped by both the verbal and cultural contexts. There are a number of verbal
contexts where expression of emotions constitutes the pivotal component of the
message. This is particularly true for User-Generated Content (UGC) which can
be in the form of a review of a product or a service, a tweet, or a social
media post. Recently, it has become common practice for multilingual websites
such as Twitter to provide an automatic translation of UGC to reach out to
their linguistically diverse users. In such scenarios, the process of
translating the user's emotion is entirely automatic with no human
intervention, neither for post-editing nor for accuracy checking. In this
research, we assess whether automatic translation tools can be a successful
real-life utility in transferring emotion in user-generated multilingual data
such as tweets. We show that there are linguistic phenomena specific of Twitter
data that pose a challenge in translation of emotions in different languages.
We summarise these challenges in a list of linguistic features and show how
frequent these features are in different language pairs. We also assess the
capacity of commonly used methods for evaluating the performance of an MT
system with respect to the preservation of emotion in the source text.