Using semantic unification to generate regular expressions from natural language

We consider the problem of translating natu-ral language text queries into regular expres-sions which represent their meaning. The mis-match in the level of abstraction between the natural language representation and the regu-lar expression representation make this a novel and challenging problem. However, a given regular expression can be written in many se-mantically equivalent forms, and we exploit this flexibility to facilitate translation by find-ing a form which more directly corresponds to the natural language. We evaluate our tech-nique on a set of natural language queries and their associated regular expressions which we gathered from Amazon Mechanical Turk. Our model substantially outperforms a state-of-the-art semantic parsing baseline, yielding a 29 % absolute improvement in accuracy.1