1,763 research outputs found
A Deep Network Model for Paraphrase Detection in Short Text Messages
This paper is concerned with paraphrase detection. The ability to detect
similar sentences written in natural language is crucial for several
applications, such as text mining, text summarization, plagiarism detection,
authorship authentication and question answering. Given two sentences, the
objective is to detect whether they are semantically identical. An important
insight from this work is that existing paraphrase systems perform well when
applied on clean texts, but they do not necessarily deliver good performance
against noisy texts. Challenges with paraphrase detection on user generated
short texts, such as Twitter, include language irregularity and noise. To cope
with these challenges, we propose a novel deep neural network-based approach
that relies on coarse-grained sentence modeling using a convolutional neural
network and a long short-term memory model, combined with a specific
fine-grained word-level similarity matching model. Our experimental results
show that the proposed approach outperforms existing state-of-the-art
approaches on user-generated noisy social media data, such as Twitter texts,
and achieves highly competitive performance on a cleaner corpus
Linear Embedding-based High-dimensional Batch Bayesian Optimization without Reconstruction Mappings
The optimization of high-dimensional black-box functions is a challenging
problem. When a low-dimensional linear embedding structure can be assumed,
existing Bayesian optimization (BO) methods often transform the original
problem into optimization in a low-dimensional space. They exploit the
low-dimensional structure and reduce the computational burden. However, we
reveal that this approach could be limited or inefficient in exploring the
high-dimensional space mainly due to the biased reconstruction of the
high-dimensional queries from the low-dimensional queries. In this paper, we
investigate a simple alternative approach: tackling the problem in the original
high-dimensional space using the information from the learned low-dimensional
structure. We provide a theoretical analysis of the exploration ability.
Furthermore, we show that our method is applicable to batch optimization
problems with thousands of dimensions without any computational difficulty. We
demonstrate the effectiveness of our method on high-dimensional benchmarks and
a real-world function
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