18 research outputs found
Task-Oriented Query Reformulation with Reinforcement Learning
Search engines play an important role in our everyday lives by assisting us
in finding the information we need. When we input a complex query, however,
results are often far from satisfactory. In this work, we introduce a query
reformulation system based on a neural network that rewrites a query to
maximize the number of relevant documents returned. We train this neural
network with reinforcement learning. The actions correspond to selecting terms
to build a reformulated query, and the reward is the document recall. We
evaluate our approach on three datasets against strong baselines and show a
relative improvement of 5-20% in terms of recall. Furthermore, we present a
simple method to estimate a conservative upper-bound performance of a model in
a particular environment and verify that there is still large room for
improvements.Comment: EMNLP 201
On the hardness of learning intersections of two halfspaces
AbstractWe show that unless NP=RP, it is hard to (even) weakly PAC-learn intersection of two halfspaces in Rn using a hypothesis which is a function of up to β halfspaces (linear threshold functions) for any integer β. Specifically, we show that for every integer β and an arbitrarily small constant Ξ΅>0, unless NP=RP, no polynomial time algorithm can distinguish whether there is an intersection of two halfspaces that correctly classifies a given set of labeled points in Rn, or whether any function of β halfspaces can correctly classify at most 12+Ξ΅ fraction of the points