ORCAS-I query intent predictor as component of TIRA

Abstract

We present a query intent predictor that is based on Snorkel weak supervision approach. After using it on ORCAS dataset and conducting a series of experiments with a variety of machine learning models we found that the results produced by Snorkel were not outperformed by these competing approaches and can be considered state-of-the-art. The advantage of a rule-based approach like Snorkel’s is its efficient deployment in an actual system, where intent classification would be executed for every query issued. When used as a component of TIRA/TIREX platform, our query intent predictor was shown to be applicable to other IR benchmark datasets. Also, we found out that the awareness of the intent overall can improve ranking results for informational intent and its subcategory factual intent.