@InProceedings{tang-EtAl:2017:RepL4NLP,
  author    = {Tang, Shuai  and  Jin, Hailin  and  Fang, Chen  and  Wang, Zhaowen  and  de Sa, Virginia},
  title     = {Rethinking Skip-thought: A Neighborhood based Approach},
  booktitle = {Proceedings of the 2nd Workshop on Representation Learning for NLP},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {211--218},
  abstract  = {We study the skip-thought model with neighborhood information as weak
	supervision. More specifically, we propose a skip-thought neighbor model to
	consider the adjacent sentences as a neighborhood. We train our skip-thought
	neighbor model on a large corpus with continuous sentences, and then evaluate
	the trained model on 7 tasks, which include semantic relatedness, paraphrase
	detection, and classification benchmarks. Both quantitative comparison and
	qualitative investigation are conducted. We empirically show that, our
	skip-thought neighbor model performs as well as the skip-thought model on
	evaluation tasks. In addition, we found that, incorporating an autoencoder path
	in our model didn't aid our model to perform better, while it hurts the
	performance of the skip-thought model.},
  url       = {http://www.aclweb.org/anthology/W17-2625}
}