Successful Machine Translation (MT) deployment requires understanding not only the intrinsic qualities of MT output, such as fluency and adequacy, but also user perceptions. Users who do not understand the source language respond to MT output based o
n their perception of the likelihood that the meaning of the MT output matches the meaning of the source text. We refer to this as believability. Output that is not believable may be off-putting to users, but believable MT output with incorrect meaning may mislead them. In this work, we study the relationship of believability to fluency and adequacy by applying traditional MT direct assessment protocols to annotate all three features on the output of neural MT systems. Quantitative analysis of these annotations shows that believability is closely related to but distinct from fluency, and initial qualitative analysis suggests that semantic features may account for the difference.
This paper presents a framework of opportunities and barriers/risks between the two research fields Natural Language Processing (NLP) and Human-Computer Interaction (HCI). The framework is constructed by following an interdisciplinary research-model
(IDR), combining field-specific knowledge with existing work in the two fields. The resulting framework is intended as a departure point for discussion and inspiration for research collaborations.
