@inproceedings{tuc-can-2020-self,
    title = "Self Attended Stack-Pointer Networks for Learning Long Term Dependencies",
    author = "Tuc, Salih  and
      Can, Burcu",
    editor = "Bhattacharyya, Pushpak  and
      Sharma, Dipti Misra  and
      Sangal, Rajeev",
    booktitle = "Proceedings of the 17th International Conference on Natural Language Processing (ICON)",
    month = dec,
    year = "2020",
    address = "Indian Institute of Technology Patna, Patna, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.icon-main.12/",
    pages = "90--100",
    abstract = "We propose a novel deep neural architecture for dependency parsing, which is built upon a Transformer Encoder (Vaswani et al. 2017) and a Stack Pointer Network (Ma et al. 2018). We first encode each sentence using a Transformer Network and then the dependency graph is generated by a Stack Pointer Network by selecting the head of each word in the sentence through a head selection process. We evaluate our model on Turkish and English treebanks. The results show that our trasformer-based model learns long term dependencies efficiently compared to sequential models such as recurrent neural networks. Our self attended stack pointer network improves UAS score around 6{\%} upon the LSTM based stack pointer (Ma et al. 2018) for Turkish sentences with a length of more than 20 words."
}