  In this paper, we introduce the use of Semantic Hashing as embedding for the
task of Intent Classification and achieve state-of-the-art performance on three
frequently used benchmarks. Intent Classification on a small dataset is a
challenging task for data-hungry state-of-the-art Deep Learning based systems.
Semantic Hashing is an attempt to overcome such a challenge and learn robust
text classification. Current word embedding based are dependent on
vocabularies. One of the major drawbacks of such methods is out-of-vocabulary
terms, especially when having small training datasets and using a wider
vocabulary. This is the case in Intent Classification for chatbots, where
typically small datasets are extracted from internet communication. Two
problems arise by the use of internet communication. First, such datasets miss
a lot of terms in the vocabulary to use word embeddings efficiently. Second,
users frequently make spelling errors. Typically, the models for intent
classification are not trained with spelling errors and it is difficult to
think about ways in which users will make mistakes. Models depending on a word
vocabulary will always face such issues. An ideal classifier should handle
spelling errors inherently. With Semantic Hashing, we overcome these challenges
and achieve state-of-the-art results on three datasets: AskUbuntu, Chatbot, and
Web Application. Our benchmarks are available online:
https://github.com/kumar-shridhar/Know-Your-Intent
