论文标题
合并查询生成的行为假设
Incorporating Behavioral Hypotheses for Query Generation
论文作者
论文摘要
生成性神经网络已在查询建议中有效。该任务通常是有条件的生成问题，旨在利用搜索会话中用户的早期输入来预测他们可能会在以后发出的查询。用户输入有多种形式，例如查询和点击，每种都可以暗示通过相应的行为模式引导的不同语义信号。本文将这些行为偏见作为查询产生的假设，其中将通用的编码器变压器框架提出以汇总选择的任意假设。我们的实验结果表明，与最近的BART模型相比，所提出的方法可导致顶级$ K $ Word错误率和BERT F1得分的显着改善。
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.
