@InProceedings{barnes-lambert-badia:2016:COLING,
  author    = {Barnes, Jeremy  and  Lambert, Patrik  and  Badia, Toni},
  title     = {Exploring Distributional Representations and Machine Translation for Aspect-based Cross-lingual Sentiment Classification.},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1613--1623},
  abstract  = {Cross-lingual sentiment classification} (CLSC) seeks to use resources from a
	source language in order to detect sentiment and classify text in a target
	language. Almost all research into CLSC has been carried out at sentence and
	document level, although this level of granularity is often less useful. This
	paper explores methods for performing aspect-based cross-lingual sentiment
	classification (aspect-based CLSC) for under-resourced languages. Given the
	limited nature of parallel data for many languages, we would like to make the
	most of this resource for our task.  We compare zero-shot learning, bilingual
	word embeddings, stacked denoising autoencoder representations and machine
	translation techniques for aspect-based CLSC. Each of these approaches requires
	differing amounts of parallel data. We show that models based on distributed
	semantics can achieve comparable results to machine translation on aspect-based
	CLSC and give an analysis of the errors found for each method.},
  url       = {http://aclweb.org/anthology/C16-1152}
}