The top word list, i.e., the top-M words with highest marginal probability in
a given topic, is the standard topic representation in topic models. Most of
recent automatical topic labeling algorithms and popular topic quality metrics
are based on it. However, we find, empirically, words in this type of top word
list are not always representative. The objective of this paper is to find more
representative top word lists for topics. To achieve this, we rerank the words
in a given topic by further considering marginal probability on words over
every other topic. The reranking list of top-M words is used to be a novel
topic representation for topic models. We investigate three reranking
methodologies, using (1) standard deviation weight, (2) standard deviation
weight with topic size and (3) Chi Square \c{hi}2statistic selection.
Experimental results on real world collections indicate that our
representations can extract more representative words for topics, agreeing with
human judgements.

本文旨在为主题模型找到更加代表性的关键词列表，通过重新排名给定主题中的单词并进一步考虑每个主题上的边际概率，将重排列的关键词列表用作主题模型的新主题呈现形式之一，实验结果表明，我们的表示可以提取更具代表性的主题词，与人类判断一致。

主题表示：在主题模型中找到更具代表性的词

Topic representation: finding more representative words in topic models

This year, the Nara Institute of Science and Technology (NAIST)'s submission
to the 2015 Workshop on Asian Translation was based on syntax-based statistical
machine translation, with the addition of a reranking component using neural
attentional machine translation models. Experiments re-confirmed results from
previous work stating that neural MT reranking provides a large gain in
objective evaluation measures such as BLEU, and also confirmed for the first
time that these results also carry over to manual evaluation. We further
perform a detailed analysis of reasons for this increase, finding that the main
contributions of the neural models lie in improvement of the grammatical
correctness of the output, as opposed to improvements in lexical choice of
content words.

研究表明，使用基于语法的统计机器翻译加上使用神经注意力机器翻译模型的重新排序组件进行机器翻译的效果较好，尤其对于语法正确性方面有较大提升。