This article presents the results of a study involving the translation of a
short story by Kurt Vonnegut from English to Catalan and Dutch using three
modalities: machine-translation (MT), post-editing (PE) and translation without
aid (HT). Our aim is to explore creativity, understood to involve novelty and
acceptability, from a quantitative perspective. The results show that HT has
the highest creativity score, followed by PE, and lastly, MT, and this is
unanimous from all reviewers. A neural MT system trained on literary data does
not currently have the necessary capabilities for a creative translation; it
renders literal solutions to translation problems. More importantly, using MT
to post-edit raw output constrains the creativity of translators, resulting in
a poorer translation often not fit for publication, according to experts.

本研究探讨了翻译写作中机器翻译，后编辑和无辅助翻译等三种方式的创造性表现，并发现经由人的自然翻译创造性最高，机器翻译需要进一步改善。

翻译中的创意：机器翻译对文学文本的约束

Creativity in translation: machine translation as a constraint for literary texts

We propose a machine-translation approach to automatically generate a
playlist title from a set of music tracks. We take a sequence of track IDs as
input and a sequence of words in a playlist title as output, adapting the
sequence-to-sequence framework based on Recurrent Neural Network (RNN) and
Transformer to the music data. Considering the orderless nature of music tracks
in a playlist, we propose two techniques that remove the order of the input
sequence. One is data augmentation by shuffling and the other is deleting the
positional encoding. We also reorganize the existing music playlist datasets to
generate phrase-level playlist titles. The result shows that the Transformer
models generally outperform the RNN model. Also, removing the order of input
sequence improves the performance further.

本文提出一种机器翻译方法，从一组音乐曲目中自动生成播放列表标题。作者采用序列到序列的框架，基于递归神经网络和 Transformer 来对音乐数据进行处理。考虑到播放列表中歌曲的无序性，作者提出两种方法来消除输入序列的顺序，即打乱和删除位置编码。结果显示，Transformer 模型通常优于 RNN 模型。此外，消除输入序列的顺序进一步提高了性能。