74 research outputs found
Cascading Randomized Weighted Majority: A New Online Ensemble Learning Algorithm
With the increasing volume of data in the world, the best approach for
learning from this data is to exploit an online learning algorithm. Online
ensemble methods are online algorithms which take advantage of an ensemble of
classifiers to predict labels of data. Prediction with expert advice is a
well-studied problem in the online ensemble learning literature. The Weighted
Majority algorithm and the randomized weighted majority (RWM) are the most
well-known solutions to this problem, aiming to converge to the best expert.
Since among some expert, the best one does not necessarily have the minimum
error in all regions of data space, defining specific regions and converging to
the best expert in each of these regions will lead to a better result. In this
paper, we aim to resolve this defect of RWM algorithms by proposing a novel
online ensemble algorithm to the problem of prediction with expert advice. We
propose a cascading version of RWM to achieve not only better experimental
results but also a better error bound for sufficiently large datasets.Comment: 15 pages, 3 figure
PCoQA: Persian Conversational Question Answering Dataset
Humans seek information regarding a specific topic through performing a
conversation containing a series of questions and answers. In the pursuit of
conversational question answering research, we introduce the PCoQA, the first
\textbf{P}ersian \textbf{Co}nversational \textbf{Q}uestion \textbf{A}nswering
dataset, a resource comprising information-seeking dialogs encompassing a total
of 9,026 contextually-driven questions. Each dialog involves a questioner, a
responder, and a document from the Wikipedia; The questioner asks several
inter-connected questions from the text and the responder provides a span of
the document as the answer for each question. PCoQA is designed to present
novel challenges compared to previous question answering datasets including
having more open-ended non-factual answers, longer answers, and fewer lexical
overlaps. This paper not only presents the comprehensive PCoQA dataset but also
reports the performance of various benchmark models. Our models include
baseline models and pre-trained models, which are leveraged to boost the
performance of the model. The dataset and benchmarks are available at our
Github page
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