A huge volume of user-generated content is daily produced on social media. To
facilitate automatic language understanding, we study keyphrase prediction,
distilling salient information from massive posts. While most existing methods
extract words from source posts to form keyphrases, we propose a
sequence-to-sequence (seq2seq) based neural keyphrase generation framework,
enabling absent keyphrases to be created. Moreover, our model, being
topic-aware, allows joint modeling of corpus-level latent topic
representations, which helps alleviate the data sparsity that widely exhibited
in social media language. Experiments on three datasets collected from English
and Chinese social media platforms show that our model significantly
outperforms both extraction and generation models that do not exploit latent
topics. Further discussions show that our model learns meaningful topics, which
interprets its superiority in social media keyphrase generation.

研究自动理解社交媒体上的用户生成内容，利用基于序列到序列模型的神经关键短语生成框架提取突出信息，并允许生成缺失关键短语。实验表明该模型在三个数据集中表现优异，能学习有意义的主题，从而解释其在社交媒体关键短语生成中的优越性。

面向话题的社交媒体语言神经关键短语生成

Topic-Aware Neural Keyphrase Generation for Social Media Language

Many classification models work poorly on short texts due to data sparsity.
To address this issue, we propose topic memory networks for short text
classification with a novel topic memory mechanism to encode latent topic
representations indicative of class labels. Different from most prior work that
focuses on extending features with external knowledge or pre-trained topics,
our model jointly explores topic inference and text classification with memory
networks in an end-to-end manner. Experimental results on four benchmark
datasets show that our model outperforms state-of-the-art models on short text
classification, meanwhile generates coherent topics.

我们提出了用于短文本分类的主题记忆网络，采用新颖的主题记忆机制来编码类别标签的潜在主题表示，实验结果表明，我们的模型在短文本分类方面优于现有模型，并生成连贯的主题。