  Understanding search queries is a hard problem as it involves dealing with
"word salad" text ubiquitously issued by users. However, if a query resembles a
well-formed question, a natural language processing pipeline is able to perform
more accurate interpretation, thus reducing downstream compounding errors.
Hence, identifying whether or not a query is well formed can enhance query
understanding. Here, we introduce a new task of identifying a well-formed
natural language question. We construct and release a dataset of 25,100
publicly available questions classified into well-formed and non-wellformed
categories and report an accuracy of 70.7% on the test set. We also show that
our classifier can be used to improve the performance of neural
sequence-to-sequence models for generating questions for reading comprehension.
