Various hetero-elements were combined with porous SnO2 spheres simply by adding hetero-element sources to the precursor solutions of optimized synthetic conditions.
Pre-editing is the process of modifying the source text (ST) so that it can be translated by machine translation (MT) in a better quality. Despite the unpredictability of black-box neural MT (NMT), pre-editing has been deployed in various practical MT use cases. Although many studies have demonstrated the effectiveness of pre-editing methods for particular settings, thus far, a deep understanding of what pre-editing is and how it works for black-box NMT is lacking. To elicit such understanding, we extensively investigated human pre-editing practices. We first implemented a protocol to incrementally record the minimum edits for each ST and collected 6,652 instances of preediting across three translation directions, two MT systems, and four text domains. We then analysed the instances from three perspectives: the characteristics of the pre-edited ST, the diversity of pre-editing operations, and the impact of the pre-editing operations on NMT outputs. Our findings include the following: (1) enhancing the explicitness of the meaning of an ST and its syntactic structure is more important for obtaining better translations than making the ST shorter and simpler, and (2) although the impact of pre-editing on NMT is generally unpredictable, there are some tendencies of changes in the NMT outputs depending on the editing operation types.
This paper presents experimental results of a usability evaluation of a controlled language (CL) authoring assistant designed to help non-professional writers create machine translatable source texts. As the author drafts the text, the system detects CL rule violations and proscribed terms. It also incorporates several support functions to facilitate rephrasing of the source. In order to assess the usability of the system, we conducted a rewriting experiment, in which we compared two groups of participants, one with the aid of the system and the other without it. The results revealed that our system helped reduce the number of CL violations by about 9% and the time to correct violations by more than 30%. The CL-applied source text resulted in higher fluency and adequacy of MT outputs. Questionnaire and interview results also implied the improved satisfaction with the task completion of those participants who used the system.
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