@InProceedings{petrovski-EtAl:2018:SCAI,
  author    = {Petrovski, Bojan  and  Aguado, Ignacio  and  Hossmann, Andreea  and  Baeriswyl, Michael  and  Musat, Claudiu},
  title     = {Embedding Individual Table Columns for Resilient SQL Chatbots},
  booktitle = {Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI},
  month     = {October},
  year      = {2018},
  address   = {Brussels, Belgium},
  publisher = {Association for Computational Linguistics},
  pages     = {67--73},
  abstract  = {Most of the world's data is stored in relational databases. Accessing these requires specialized knowledge of the Structured Query Language (SQL), putting them out of the reach of many people. A recent research thread in Natural Language Processing (NLP) aims to alleviate this problem, by automatically translating natural language questions into SQL queries. While the proposed solutions are a great start, they lack robustness and do not easily generalize: the methods require high quality descriptions of the database table columns, and the most widely used training dataset, WikiSQL, is heavily biased towards using those descriptions as part of the questions.},
  url       = {http://www.aclweb.org/anthology/W18-5710}
}