445 research outputs found
Paraphrase Generation with Deep Reinforcement Learning
Automatic generation of paraphrases from a given sentence is an important yet
challenging task in natural language processing (NLP), and plays a key role in
a number of applications such as question answering, search, and dialogue. In
this paper, we present a deep reinforcement learning approach to paraphrase
generation. Specifically, we propose a new framework for the task, which
consists of a \textit{generator} and an \textit{evaluator}, both of which are
learned from data. The generator, built as a sequence-to-sequence learning
model, can produce paraphrases given a sentence. The evaluator, constructed as
a deep matching model, can judge whether two sentences are paraphrases of each
other. The generator is first trained by deep learning and then further
fine-tuned by reinforcement learning in which the reward is given by the
evaluator. For the learning of the evaluator, we propose two methods based on
supervised learning and inverse reinforcement learning respectively, depending
on the type of available training data. Empirical study shows that the learned
evaluator can guide the generator to produce more accurate paraphrases.
Experimental results demonstrate the proposed models (the generators)
outperform the state-of-the-art methods in paraphrase generation in both
automatic evaluation and human evaluation.Comment: EMNLP 201
Small data global regularity for simplified 3-D Ericksen-Leslie's compressible hyperbolic liquid crystal model
In this article, we consider the Ericksen-Leslie's hyperbolic system for
compressible liquid crystal model in three spatial dimensions. Global
regularity for small and smooth initial data near equilibrium is proved for the
case that the system is a nonlinear coupling of compressible Navier-Stokes
equations with wave map to . Our argument is a combination of
vector field method and Fourier analysis. The main strategy to prove global
regularity relies on an interplay between the control of high order energies
and decay estimates, which is based on the idea inspired by the method of
space-time resonances. In particular the different behaviors of the decay
properties of the density and velocity field for compressible fluids at
different frequencies play a key role.Comment: 61 pages; all comments wellcom
Cognitive Medium Access: Exploration, Exploitation and Competition
This paper establishes the equivalence between cognitive medium access and
the competitive multi-armed bandit problem. First, the scenario in which a
single cognitive user wishes to opportunistically exploit the availability of
empty frequency bands in the spectrum with multiple bands is considered. In
this scenario, the availability probability of each channel is unknown to the
cognitive user a priori. Hence efficient medium access strategies must strike a
balance between exploring the availability of other free channels and
exploiting the opportunities identified thus far. By adopting a Bayesian
approach for this classical bandit problem, the optimal medium access strategy
is derived and its underlying recursive structure is illustrated via examples.
To avoid the prohibitive computational complexity of the optimal strategy, a
low complexity asymptotically optimal strategy is developed. The proposed
strategy does not require any prior statistical knowledge about the traffic
pattern on the different channels. Next, the multi-cognitive user scenario is
considered and low complexity medium access protocols, which strike the optimal
balance between exploration and exploitation in such competitive environments,
are developed. Finally, this formalism is extended to the case in which each
cognitive user is capable of sensing and using multiple channels
simultaneously.Comment: Submitted to IEEE/ACM Trans. on Networking, 14 pages, 2 figure
Neural Generative Question Answering
This paper presents an end-to-end neural network model, named Neural
Generative Question Answering (GENQA), that can generate answers to simple
factoid questions, based on the facts in a knowledge-base. More specifically,
the model is built on the encoder-decoder framework for sequence-to-sequence
learning, while equipped with the ability to enquire the knowledge-base, and is
trained on a corpus of question-answer pairs, with their associated triples in
the knowledge-base. Empirical study shows the proposed model can effectively
deal with the variations of questions and answers, and generate right and
natural answers by referring to the facts in the knowledge-base. The experiment
on question answering demonstrates that the proposed model can outperform an
embedding-based QA model as well as a neural dialogue model trained on the same
data.Comment: Accepted by IJCAI 201
A comparative study of two molecular mechanics models based on harmonic potentials
We show that the two molecular mechanics models, the stick-spiral and the
beam models, predict considerably different mechanical properties of materials
based on energy equivalence. The difference between the two models is
independent of the materials since all parameters of the beam model are
obtained from the harmonic potentials. We demonstrate this difference for
finite width graphene nanoribbons and a single polyethylene chain comparing
results of the molecular dynamics (MD) simulations with harmonic potentials and
the finite element method with the beam model. We also find that the difference
strongly depends on the loading modes, chirality and width of the graphene
nanoribbons, and it increases with decreasing width of the nanoribbons under
pure bending condition. The maximum difference of the predicted mechanical
properties using the two models can exceed 300% in different loading modes.
Comparing the two models with the MD results of AIREBO potential, we find that
the stick-spiral model overestimates and the beam model underestimates the
mechanical properties in narrow armchair graphene nanoribbons under pure
bending condition.Comment: 40 pages, 21 figure
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