@InProceedings{ravfogel-goldberg-tyers:2018:BlackboxNLP,
  author    = {Ravfogel, Shauli  and  Goldberg, Yoav  and  Tyers, Francis},
  title     = {Can LSTM Learn to Capture Agreement? The Case of Basque},
  booktitle = {Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP},
  month     = {November},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {98--107},
  abstract  = {We focus on the task of agreement prediction in Basque, as a case study for a task that requires implicit understanding of sentence structure and the acquisition of a complex but consistent morphological system. In a series of controlled experiments, we probe the ability of sequential models to learn agreement patterns and asses different aspects of the problem. Analyzing experimental results from two syntactic prediction tasks -- verb number prediction and suffix recovery -- we find that sequential models perform worse on agreement prediction in Basque than one might expect on the basis of a previous agreement prediction work in English. Tentative findings based on diagnostic classifiers suggest the network makes use of local heuristics as a proxy for the hierarchical structure of the sentence. We propose the Basque agreement prediction task as challenging benchmark for models that attempt to learn regularities in human language.},
  url       = {http://www.aclweb.org/anthology/W18-5412}
}