@inproceedings{zhang-moldovan-2018-rule,
    title = "Rule-based vs. Neural Net Approaches to Semantic Textual Similarity",
    author = "Zhang, Linrui  and
      Moldovan, Dan",
    editor = "Machonis, Peter  and
      Barreiro, Anabela  and
      Kocijan, Kristina  and
      Silberztein, Max",
    booktitle = "Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W18-3803/",
    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."
}