145 research outputs found
Towards Abstractive Timeline Summarisation using Preference-based Reinforcement Learning
This paper introduces a novel pipeline for summarising timelines of events
reported by multiple news sources. Transformer-based models for abstractive
summarisation generate coherent and concise summaries of long documents but can
fail to outperform established extractive methods on specialised tasks such as
timeline summarisation (TLS). While extractive summaries are more faithful to
their sources, they may be less readable and contain redundant or unnecessary
information. This paper proposes a preference-based reinforcement learning
(PBRL) method for adapting pretrained abstractive summarisers to TLS, which can
overcome the drawbacks of extractive timeline summaries. We define a compound
reward function that learns from keywords of interest and pairwise preference
labels, which we use to fine-tune a pretrained abstractive summariser via
offline reinforcement learning. We carry out both automated and human
evaluation on three datasets, finding that our method outperforms a comparable
extractive TLS method on two of the three benchmark datasets, and participants
prefer our method's summaries to those of both the extractive TLS method and
the pretrained abstractive model. The method does not require expensive
reference summaries and needs only a small number of preferences to align the
generated summaries with human preferences.Comment: ECAI 202
Awesome-META+: Meta-Learning Research and Learning Platform
Artificial intelligence technology has already had a profound impact in
various fields such as economy, industry, and education, but still limited.
Meta-learning, also known as "learning to learn", provides an opportunity for
general artificial intelligence, which can break through the current AI
bottleneck. However, meta learning started late and there are fewer projects
compare with CV, NLP etc. Each deployment requires a lot of experience to
configure the environment, debug code or even rewrite, and the frameworks are
isolated. Moreover, there are currently few platforms that focus exclusively on
meta-learning, or provide learning materials for novices, for which the
threshold is relatively high. Based on this, Awesome-META+, a meta-learning
framework integration and learning platform is proposed to solve the above
problems and provide a complete and reliable meta-learning framework
application and learning platform. The project aims to promote the development
of meta-learning and the expansion of the community, including but not limited
to the following functions: 1) Complete and reliable meta-learning framework,
which can adapt to multi-field tasks such as target detection, image
classification, and reinforcement learning. 2) Convenient and simple model
deployment scheme which provide convenient meta-learning transfer methods and
usage methods to lower the threshold of meta-learning and improve efficiency.
3) Comprehensive researches for learning. 4) Objective and credible performance
analysis and thinking
UrbanFM: Inferring Fine-Grained Urban Flows
Urban flow monitoring systems play important roles in smart city efforts
around the world. However, the ubiquitous deployment of monitoring devices,
such as CCTVs, induces a long-lasting and enormous cost for maintenance and
operation. This suggests the need for a technology that can reduce the number
of deployed devices, while preventing the degeneration of data accuracy and
granularity. In this paper, we aim to infer the real-time and fine-grained
crowd flows throughout a city based on coarse-grained observations. This task
is challenging due to two reasons: the spatial correlations between coarse- and
fine-grained urban flows, and the complexities of external impacts. To tackle
these issues, we develop a method entitled UrbanFM based on deep neural
networks. Our model consists of two major parts: 1) an inference network to
generate fine-grained flow distributions from coarse-grained inputs by using a
feature extraction module and a novel distributional upsampling module; 2) a
general fusion subnet to further boost the performance by considering the
influences of different external factors. Extensive experiments on two
real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness
and efficiency of our method compared to seven baselines, demonstrating the
state-of-the-art performance of our approach on the fine-grained urban flow
inference problem
Integrating Relation Constraints with Neural Relation Extractors
Recent years have seen rapid progress in identifying predefined relationship
between entity pairs using neural networks NNs. However, such models often make
predictions for each entity pair individually, thus often fail to solve the
inconsistency among different predictions, which can be characterized by
discrete relation constraints. These constraints are often defined over
combinations of entity-relation-entity triples, since there often lack of
explicitly well-defined type and cardinality requirements for the relations. In
this paper, we propose a unified framework to integrate relation constraints
with NNs by introducing a new loss term, ConstraintLoss. Particularly, we
develop two efficient methods to capture how well the local predictions from
multiple instance pairs satisfy the relation constraints. Experiments on both
English and Chinese datasets show that our approach can help NNs learn from
discrete relation constraints to reduce inconsistency among local predictions,
and outperform popular neural relation extraction NRE models even enhanced with
extra post-processing. Our source code and datasets will be released at
https://github.com/PKUYeYuan/Constraint-Loss-AAAI-2020.Comment: Accepted to AAAI-202
Luminescence of delafossite-type CuAlO2 fibers with Eu substitution for Al cations
CuAlO2 has been examined as a potential luminescent material by substituting Eu for Al cations in the delafossite structure. CuAlO2:Eu3+ nanofibers have been prepared via electrospinning for the ease of mitigating synthesis requirements and for future optoelectronics and emerging applications. Single-phase CuAlO2 fibers could be obtained at a temperature of 1100 °C in air. The Eu was successfully doped in the delafossite structure and two strong emission bands at ~405 and 610 nm were observed in the photoluminescence spectra. These bands are due to the intrinsic near-band-edge transition of CuAlO2 and the f-f transition of the Eu3+ activator, respectively. Further electrical characterization indicated that these fibers exhibit semiconducting behavior and the introduction of Eu could act as band-edge modifiers, thus changing the thermal activation energies. In light of this study, CuAlO2:Eu3+ fibers with both strong photoluminescence and p-type conductivity could be produced by tailoring the rare earth doping concentrations
Neural topic modeling with bidirectional adversarial training
Recent years have witnessed a surge of interests of using neural topic models for automatic topic extraction from text, since they avoid the complicated mathematical derivations for model inference as in traditional topic models such as Latent Dirichlet Allocation (LDA). However, these models either typically assume improper prior (e.g. Gaussian or Logistic Normal) over latent topic space or could not infer topic distribution for a given document. To address these limitations, we propose a neural topic modeling approach, called Bidirectional Adversarial Topic (BAT) model, which represents the first attempt of applying bidirectional adversarial training for neural topic modeling. The proposed BAT builds a two-way projection between the document-topic distribution and the document-word distribution. It uses a generator to capture the semantic patterns from texts and an encoder for topic inference. Furthermore, to incorporate word relatedness information, the Bidirectional Adversarial Topic model with Gaussian (Gaussian-BAT) is extended from BAT. To verify the effectiveness of BAT and Gaussian-BAT, three benchmark corpora are used in our experiments. The experimental results show that BAT and Gaussian-BAT obtain more coherent topics, outperforming several competitive baselines. Moreover, when performing text clustering based on the extracted topics, our models outperform all the baselines, with more significant improvements achieved by Gaussian-BAT where an increase of near 6% is observed in accuracy
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