413 research outputs found
A generalised alignment template formalism and its application to the inference of shallow-transfer machine translation rules from scarce bilingual corpora
Statistical and rule-based methods are complementary approaches to machine translation (MT) that have different strengths and weaknesses. This complementarity has, over the last few years, resulted in the consolidation of a growing interest in hybrid systems that combine both data-driven and linguistic approaches. In this paper, we address the situation in which the amount of bilingual resources that is available for a particular language pair is not sufficiently large to train a competitive statistical MT system, but the cost and slow development cycles of rule-based MT systems cannot be afforded either. In this context, we formalise a new method that uses scarce parallel corpora to automatically infer a set of shallow-transfer rules to be integrated into a rule-based MT system, thus avoiding the need for human experts to handcraft these rules. Our work is based on the alignment template approach to phrase-based statistical MT, but the definition of the alignment template is extended to encompass different generalisation levels. It is also greatly inspired by the work of Sánchez-MartĂnez and Forcada (2009) in which alignment templates were also considered for shallow-transfer rule inference. However, our approach overcomes many relevant limitations of that work, principally those related to the inability to find the correct generalisation level for the alignment templates, and to select the subset of alignment templates that ensures an adequate segmentation of the input sentences by the rules eventually obtained. Unlike previous approaches in literature, our formalism does not require linguistic knowledge about the languages involved in the translation. Moreover, it is the first time that conflicts between rules are resolved by choosing the most appropriate ones according to a global minimisation function rather than proceeding in a pairwise greedy fashion. Experiments conducted using five different language pairs with the free/open-source rule-based MT platform Apertium show that translation quality significantly improves when compared to the method proposed by Sánchez-MartĂnez and Forcada (2009), and is close to that obtained using handcrafted rules. For some language pairs, our approach is even able to outperform them. Moreover, the resulting number of rules is considerably smaller, which eases human revision and maintenance.Research funded by Universitat d’Alacant through project GRE11-20, by the Spanish Ministry of Economy and Competitiveness through projects TIN2009-14009-C02-01 and TIN2012-32615, by Generalitat Valenciana through grant ACIF/2010/174, and by the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement PIAP-GA-2012-324414 (Abu-MaTran)
DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases
Keyphrase extraction from documents is useful to a variety of applications
such as information retrieval and document summarization. This paper presents
an end-to-end method called DivGraphPointer for extracting a set of diversified
keyphrases from a document. DivGraphPointer combines the advantages of
traditional graph-based ranking methods and recent neural network-based
approaches. Specifically, given a document, a word graph is constructed from
the document based on word proximity and is encoded with graph convolutional
networks, which effectively capture document-level word salience by modeling
long-range dependency between words in the document and aggregating multiple
appearances of identical words into one node. Furthermore, we propose a
diversified point network to generate a set of diverse keyphrases out of the
word graph in the decoding process. Experimental results on five benchmark data
sets show that our proposed method significantly outperforms the existing
state-of-the-art approaches.Comment: Accepted to SIGIR 201
A Review of Graph Neural Networks and Their Applications in Power Systems
Deep neural networks have revolutionized many machine learning tasks in power
systems, ranging from pattern recognition to signal processing. The data in
these tasks is typically represented in Euclidean domains. Nevertheless, there
is an increasing number of applications in power systems, where data are
collected from non-Euclidean domains and represented as graph-structured data
with high dimensional features and interdependency among nodes. The complexity
of graph-structured data has brought significant challenges to the existing
deep neural networks defined in Euclidean domains. Recently, many publications
generalizing deep neural networks for graph-structured data in power systems
have emerged. In this paper, a comprehensive overview of graph neural networks
(GNNs) in power systems is proposed. Specifically, several classical paradigms
of GNNs structures (e.g., graph convolutional networks) are summarized, and key
applications in power systems, such as fault scenario application, time series
prediction, power flow calculation, and data generation are reviewed in detail.
Furthermore, main issues and some research trends about the applications of
GNNs in power systems are discussed
AN INVESTIGATION INTO ADAPTIVE SEARCH TECHNIQUES FOR THE AUTOMATIC GENERATION OF SOFTWARE TEST DATA
The focus of this thesis is on the use of adaptive search techniques for the automatic
generation of software test data. Three adaptive search techniques are used, these are
genetic algorithms (GAs), Simulated Amiealing and Tabu search. In addition to
these, hybrid search methods have been developed and applied to the problem of test
data generation. The adaptive search techniques are compared to random generation
to ascertain the effectiveness of adaptive search. The results indicate that GAs and
Simulated Annealing outperform random generation in all test programs. Tabu
search outperformed random generation in most tests, but it lost its effectiveness as
the amount of input data increased. The hybrid techniques have given mixed results.
The two best methods, GAs and Simulated Annealing are then compared to random
generation on a program written to optimise capital budgeting, both perform better
than random generation and Simulated Annealing requires less test data than GAs.
Further research highlights a need for research into the control parameters of all the
adaptive search methods and attaining test data which covers border conditions
AMPNet: Attention as Message Passing for Graph Neural Networks
Graph Neural Networks (GNNs) have emerged as a powerful representation
learning framework for graph-structured data. A key limitation of conventional
GNNs is their representation of each node with a singular feature vector,
potentially overlooking intricate details about individual node features. Here,
we propose an Attention-based Message-Passing layer for GNNs (AMPNet) that
encodes individual features per node and models feature-level interactions
through cross-node attention during message-passing steps. We demonstrate the
abilities of AMPNet through extensive benchmarking on real-world biological
systems such as fMRI brain activity recordings and spatial genomic data,
improving over existing baselines by 20% on fMRI signal reconstruction, and
further improving another 8% with positional embedding added. Finally, we
validate the ability of AMPNet to uncover meaningful feature-level interactions
through case studies on biological systems. We anticipate that our architecture
will be highly applicable to graph-structured data where node entities
encompass rich feature-level information.Comment: 16 pages (12 + 4 pages appendix). 5 figures and 7 table
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