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Inductive Transfer Learning for Detection of Well-formed Natural Language Search QueriesAuthors: Bakhtiyar Syed,Vijaysaradhi Indurthi,Manish Gupta,Manish Shrivastava,Vasudeva Varma Conference: 41st European Conference on Information Retrieval (ECIR-2019 2019) Location Cologne, Germany Date: 2019-04-14 Report no: IIIT/TR/2019/27 AbstractUsers have been trained to type keyword queries on search engines. However, recently there has been a significant rise in the number of verbose queries. Often times such queries are not well-formed. The lack of well-formedness in the query might adversely impact the downstream pipeline which processes these queries. A well-formed natural language question as a search query aids heavily in reducing errors in downstream tasks and further helps in improved query understanding. In this paper, we employ an inductive transfer learning technique by fine-tuning a pretrained language model to identify whether a search query is a well-formed natural language question or not. We show that our model trained on a recently released benchmark dataset spanning 25,100 queries gives an accuracy of 75.03% thereby improving by ∼5 absolute percentage points over the state-of-the-art. Full paper: pdf Centre for Search and Information Extraction Lab |
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