Identification of the parallel documents from multilingual news websites

We present the initial results of our experiments on document alignment for the online news domain. Specifically, as apposed to cross-site comparable news alignment, we focus on the identification of parallel documents from within the same multilingual websites. In such a setting parallel news stories oftentimes turn out to be direct translations of each other with a tendency of sharing common media and displaying proximity in publication date. We leverage this domain-specific property of the data and propose a straightforward yet competitive heuristic that performs on par with a machine learning-based method in terms of precision, and outperforms a widely used bitext extraction system on a range of metrics. Moreover, this heuristic has allowed us to identify comparable documents overlooked by a human annotator. Although both rule-and learning-based methods that we present are language independent, we specifically focus on the Russian-Kazakh language pair as the present study is one of the initial steps towards a greater objective of building a corresponding parallel corpus and a machine translation system.