Abstract
Users 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 \(\sim \)5 absolute percentage points over the state-of-the-art.
B. Syed and V. Indurthi—Authors contributed equally.
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Notes
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A rating greater than or equal to 0.8 ensures at least 4 out of 5 annotators marked the query as well-formed.
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Syed, B., Indurthi, V., Gupta, M., Shrivastava, M., Varma, V. (2019). Inductive Transfer Learning for Detection of Well-Formed Natural Language Search Queries. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11438. Springer, Cham. https://doi.org/10.1007/978-3-030-15719-7_6
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