Users often query a search engine with a specific question in mind and often these queries are keywords or sub-sentential fragments. In this paper, we are proposing a method to generate well-formed natural language question from a given keyword-based query, which has the same question intent as the query.Conversion of keyword based web query into a well formed question has lots of applications in search engines, Community Question Answering (CQA) website and bots communication. We found a synergy between query-to-question problem with standard machine translation (MT) task. We have used both Statistical MT (SMT) and Neural MT(NMT) models to generate the questions from query. We have observed that MT models performs well in terms of both automatic and human evaluation.
@InProceedings{KUMAR18.805, author = {Adarsh Kumar and Sandipan Dandapat and Sushil Chordia}, title = "{Translating Web Search Queries into Natural Language Questions}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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