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Generative neural networks have been shown effective on query suggestion.Commonly posed as a conditional generation problem, the task aims to leverageearlier inputs from users in a search session to predict queries that they willlikely issue at a later time. User inputs come in various forms such asquerying and clicking, each of which can imply different semantic signalschanneled through the corresponding behavioral patterns. This paper inducesthese behavioral biases as hypotheses for query generation, where a genericencoder-decoder Transformer framework is presented to aggregate arbitraryhypotheses of choice. Our experimental results show that the proposed approachleads to significant improvements on top-$k$ word error rate and Bert F1 Scorecompared to a recent BART model.

Incorporating Behavioral Hypotheses for Query Generation