The paper steps outside the comfort-zone of the traditional NLP tasks like
automatic speech recognition (ASR) and machine translation (MT) to addresses
two novel problems arising in the automated multilingual news monitoring:
segmentation of the TV and radio program ASR transcripts into individual
stories, and clustering of the individual stories coming from various sources
and languages into storylines. Storyline clustering of stories covering the
same events is an essential task for inquisitorial media monitoring. We address
these two problems jointly by engaging the low-dimensional semantic
representation capabilities of the sequence to sequence neural translation
models. To enable joint multi-task learning for multilingual neural translation
of morphologically rich languages we replace the attention mechanism with the
sliding-window mechanism and operate the sequence to sequence neural
translation model on the character-level rather than on the word-level. The
story segmentation and storyline clustering problem is tackled by examining the
low-dimensional vectors produced as a side-product of the neural translation
process. The results of this paper describe a novel approach to the automatic
story segmentation and storyline clustering problem.

通过使用序列到序列的神经翻译模型的低维语义表示能力，该论文尝试解决自动多语种新闻监测中出现的两个问题：将电视和广播节目 ASR 转录分割成单个故事，对来自各种来源和语言的单个故事进行故事线聚类。为了实现多语种神经翻译的联合多任务学习，论文使用滑动窗口机制替换注意力机制，并在字符级别而非单词级别上操作序列到序列的神经翻译模型处理分割和聚类问题。通过检查作为神经翻译过程副产品产生的低维向量，解决分割故事和故事线聚类问题的方法值得进一步研究。