103,540 research outputs found
Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs
Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA. This problem is most pronounced when answers can be found only by joining evidence from multiple documents. Curated knowledge graphs (KGs) may yield good answers, but are limited by their inherent incompleteness and potential staleness. This paper presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents. Our method is completely unsupervised, avoiding training-data bottlenecks and being able to cope with rapidly evolving ad hoc topics and formulation style in user questions. QUEST builds a noisy quasi KG with node and edge weights, consisting of dynamically retrieved entity names and relational phrases. It augments this graph with types and semantic alignments, and computes the best answers by an algorithm for Group Steiner Trees. We evaluate QUEST on benchmarks of complex questions, and show that it substantially outperforms state-of-the-art baselines
Abstracting Fairness: Oracles, Metrics, and Interpretability
It is well understood that classification algorithms, for example, for
deciding on loan applications, cannot be evaluated for fairness without taking
context into account. We examine what can be learned from a fairness oracle
equipped with an underlying understanding of ``true'' fairness. The oracle
takes as input a (context, classifier) pair satisfying an arbitrary fairness
definition, and accepts or rejects the pair according to whether the classifier
satisfies the underlying fairness truth. Our principal conceptual result is an
extraction procedure that learns the underlying truth; moreover, the procedure
can learn an approximation to this truth given access to a weak form of the
oracle. Since every ``truly fair'' classifier induces a coarse metric, in which
those receiving the same decision are at distance zero from one another and
those receiving different decisions are at distance one, this extraction
process provides the basis for ensuring a rough form of metric fairness, also
known as individual fairness. Our principal technical result is a higher
fidelity extractor under a mild technical constraint on the weak oracle's
conception of fairness. Our framework permits the scenario in which many
classifiers, with differing outcomes, may all be considered fair. Our results
have implications for interpretablity -- a highly desired but poorly defined
property of classification systems that endeavors to permit a human arbiter to
reject classifiers deemed to be ``unfair'' or illegitimately derived.Comment: 17 pages, 1 figur
Predictive User Modeling with Actionable Attributes
Different machine learning techniques have been proposed and used for
modeling individual and group user needs, interests and preferences. In the
traditional predictive modeling instances are described by observable
variables, called attributes. The goal is to learn a model for predicting the
target variable for unseen instances. For example, for marketing purposes a
company consider profiling a new user based on her observed web browsing
behavior, referral keywords or other relevant information. In many real world
applications the values of some attributes are not only observable, but can be
actively decided by a decision maker. Furthermore, in some of such applications
the decision maker is interested not only to generate accurate predictions, but
to maximize the probability of the desired outcome. For example, a direct
marketing manager can choose which type of a special offer to send to a client
(actionable attribute), hoping that the right choice will result in a positive
response with a higher probability. We study how to learn to choose the value
of an actionable attribute in order to maximize the probability of a desired
outcome in predictive modeling. We emphasize that not all instances are equally
sensitive to changes in actions. Accurate choice of an action is critical for
those instances, which are on the borderline (e.g. users who do not have a
strong opinion one way or the other). We formulate three supervised learning
approaches for learning to select the value of an actionable attribute at an
instance level. We also introduce a focused training procedure which puts more
emphasis on the situations where varying the action is the most likely to take
the effect. The proof of concept experimental validation on two real-world case
studies in web analytics and e-learning domains highlights the potential of the
proposed approaches
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