Boosting Search Engines with Interactive Agents
- URL: http://arxiv.org/abs/2109.00527v1
- Date: Wed, 1 Sep 2021 13:11:57 GMT
- Title: Boosting Search Engines with Interactive Agents
- Authors: Leonard Adolphs, Benjamin Boerschinger, Christian Buck, Michelle Chen
Huebscher, Massimiliano Ciaramita, Lasse Espeholt, Thomas Hofmann, Yannic
Kilcher
- Abstract summary: This paper presents first steps in designing agents that learn meta-strategies for contextual query refinements.
Agents are empowered with simple but effective search operators to exert fine-grained and transparent control over queries and search results.
- Score: 25.89284695491093
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Can machines learn to use a search engine as an interactive tool for finding
information? That would have far reaching consequences for making the world's
knowledge more accessible. This paper presents first steps in designing agents
that learn meta-strategies for contextual query refinements. Our approach uses
machine reading to guide the selection of refinement terms from aggregated
search results. Agents are then empowered with simple but effective search
operators to exert fine-grained and transparent control over queries and search
results. We develop a novel way of generating synthetic search sessions, which
leverages the power of transformer-based generative language models through
(self-)supervised learning. We also present a reinforcement learning agent with
dynamically constrained actions that can learn interactive search strategies
completely from scratch. In both cases, we obtain significant improvements over
one-shot search with a strong information retrieval baseline. Finally, we
provide an in-depth analysis of the learned search policies.
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