Given the rise of a new approach to MT, Neural MT (NMT), and its promising
performance on different text types, we assess the translation quality it can
attain on what is perceived to be the greatest challenge for MT: literary text.
Specifically, we target novels, arguably the most popular type of literary
text. We build a literary-adapted NMT system for the English-to-Catalan
translation direction and evaluate it against a system pertaining to the
previous dominant paradigm in MT: statistical phrase-based MT (PBSMT). To this
end, for the first time we train MT systems, both NMT and PBSMT, on large
amounts of literary text (over 100 million words) and evaluate them on a set of
twelve widely known novels spanning from the the 1920s to the present day.
According to the BLEU automatic evaluation metric, NMT is significantly better
than PBSMT (p < 0.01) on all the novels considered. Overall, NMT results in a
11% relative improvement (3 points absolute) over PBSMT. A complementary human
evaluation on three of the books shows that between 17% and 34% of the
translations, depending on the book, produced by NMT (versus 8% and 20% with
PBSMT) are perceived by native speakers of the target language to be of
equivalent quality to translations produced by a professional human translator.

本文基于神经机器翻译（NMT）和短语统计机器翻译（PBSMT）训练了英语到加泰罗尼亚语的文学适应翻译系统，并在 12 部文学作品上进行了评估，结果表明基于 NMT 的系统相对于 PBSMT 的结果有显著的提升（p<0.01）, 其自动 BLEU 评估指标提高了 11%（3 个点），而人类的评估结果也证明了 NMT 翻译结果的优势。

神经机器翻译在文学文本上能达到什么质量水平？

What Level of Quality can Neural Machine Translation Attain on Literary  Text?

We apply entropy agglomeration (EA), a recently introduced algorithm, to
cluster the words of a literary text. EA is a greedy agglomerative procedure
that minimizes projection entropy (PE), a function that can quantify the
segmentedness of an element set. To apply it, the text is reduced to a feature
allocation, a combinatorial object to represent the word occurences in the
text's paragraphs. The experiment results demonstrate that EA, despite its
reduction and simplicity, is useful in capturing significant relationships
among the words in the text. This procedure was implemented in Python and
published as a free software: REBUS.

应用熵聚类算法对文学文本进行聚类分析，实验结果表明该算法具有在捕捉文本中单词之间显著关系方面的实用性。