Incorporating Behavioral Hypotheses for Query Generation

• URL: http://arxiv.org/abs/2010.02667v1
• Date: Tue, 6 Oct 2020 12:38:02 GMT
• Title: Incorporating Behavioral Hypotheses for Query Generation
• Authors: Ruey-Cheng Chen, Chia-Jung Lee
• Abstract summary: We propose a generic encoder-decoder Transformer framework to aggregate arbitrary hypotheses of choice for query generation. Our experimental results show that the proposed approach leads to significant improvements on top-$k$ word error rate and Bert F1 Score.
• Score: 3.5873361386290643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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