  Generative neural networks have been shown effective on query suggestion.
Commonly posed as a conditional generation problem, the task aims to leverage
earlier inputs from users in a search session to predict queries that they will
likely issue at a later time. User inputs come in various forms such as
querying and clicking, each of which can imply different semantic signals
channeled through the corresponding behavioral patterns. This paper induces
these behavioral biases as hypotheses for query generation, where a generic
encoder-decoder Transformer framework is presented to aggregate arbitrary
hypotheses of choice. Our experimental results show that the proposed approach
leads to significant improvements on top-$k$ word error rate and Bert F1 Score
compared to a recent BART model.
