@InProceedings{grover-mitra:2017:BUCC,
  author    = {Grover, Jeenu  and  Mitra, Pabitra},
  title     = {Sentence Alignment using Unfolding Recursive Autoencoders},
  booktitle = {Proceedings of the 10th Workshop on Building and Using Comparable Corpora},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {16--20},
  abstract  = {In this paper, we propose a novel two step algorithm for sentence alignment in
	monolingual corpora using Unfolding Recursive Autoencoders. First, we use
	unfolding recursive auto-encoders (RAE) to learn feature vectors for phrases in
	syntactical tree of the sentence. To compare two sentences we use a similarity
	matrix which has dimensions proportional to the size of the two sentences.
	Since the similarity matrix generated to compare two sentences has varying
	dimension due to different sentence lengths, a dynamic pooling layer is used to
	map it to a matrix of fixed dimension. The resulting matrix is used to
	calculate the similarity scores between the two sentences. The second step of
	the algorithm captures the contexts in which the sentences occur in the
	document by using a dynamic programming algorithm for global alignment.},
  url       = {http://www.aclweb.org/anthology/W17-2503}
}