Incorporating Behavioral Hypotheses for Query Generation
This work addresses query suggestion for search engines, but it is incremental as it builds on existing encoder-decoder methods with behavioral hypotheses.
The paper tackled the problem of generating user queries in search sessions by incorporating behavioral biases as hypotheses into a Transformer framework, resulting in significant improvements in top-k word error rate and BERT F1 score compared to a BART model.
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.