Lexical Learning as an Online Optimal Experiment: Building Efficient Search Engines through Human-Machine Collaboration
This work addresses the challenge of adapting search engines to evolving user queries and data, though it appears incremental as it builds on existing human-in-the-loop concepts with preliminary results.
The authors tackled the problem of updating lexical knowledge in information retrieval systems by proposing a novel human-in-the-loop framework that combines psycholinguistics and experiment design, with initial simulations in a toy world demonstrating the inference process.
Information retrieval (IR) systems need to constantly update their knowledge as target objects and user queries change over time. Due to the power-law nature of linguistic data, learning lexical concepts is a problem resisting standard machine learning approaches: while manual intervention is always possible, a more general and automated solution is desirable. In this work, we propose a novel end-to-end framework that models the interaction between a search engine and users as a virtuous human-in-the-loop inference. The proposed framework is the first to our knowledge combining ideas from psycholinguistics and experiment design to maximize efficiency in IR. We provide a brief overview of the main components and initial simulations in a toy world, showing how inference works end-to-end and discussing preliminary results and next steps.