  We present a method for coarse-grained cross-lingual alignment of comparable
texts: segments consisting of contiguous paragraphs that discuss the same theme
(e.g. history, economy) are aligned based on induced multilingual topics. The
method combines three ideas: a two-level LDA model that filters out words that
do not convey themes, an HMM that models the ordering of themes in the
collection of documents, and language-independent concept annotations to serve
as a cross-language bridge and to strengthen the connection between paragraphs
in the same segment through concept relations. The method is evaluated on
English and French data previously used for monolingual alignment. The results
show state-of-the-art performance in both monolingual and cross-lingual
settings.
