@InProceedings{zhang-moldovan:2018:W18-38,
  author    = {Zhang, Linrui  and  Moldovan, Dan},
  title     = {Rule-based vs. Neural Net Approaches to Semantic Textual Similarity},
  booktitle = {Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {12--17},
  abstract  = {This paper presents a neural net approach to determine Semantic Textual Similarity (STS) using attention-based bidirectional Long Short-Term Memory Networks (Bi-LSTM). To this date, most of the traditional STS systems were rule-based that built on top of excessive use of linguistic features and resources. In this paper, we present an end-to-end attention-based Bi-LSTM neural network system that solely takes word-level features, without expensive feature engineering work or the usage of external resources. By comparing its performance with traditional rule-based systems against SemEval-2012 benchmark, we make an assessment on the limitations and strengths of neural net systems to rule-based systems on Semantic Textual Similarity.},
  url       = {http://www.aclweb.org/anthology/W18-3803}
}