Neural models
Lexicosyntactic Inference in Neural Models
... state-of-the-art neural systems for a subtask of general event extraction – event factuality predic- tion (EFP; Nairn et ...understanding neural systems’ behav- ior more generally because (i) the best ...
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Visualizing and Understanding Neural Models in NLP
... seen, neural models nicely learn the properties of local compositionally, clustering negation+positive words (‘not nice’, ’not good’) together with negative ...the neural model is not just learning ...
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Evolving neural models of path integration
... recurrent neural circuit (Seung, 1996). Whatever the neural basis, functionally the integrator can display leaky, stable or unstable behaviours (Major et ...employing neural models capable of ...
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Neural Models of the Psychosemantics of ‘Most’
... the models to generate ...the models are using to solve the task, techniques such as transfer learn- ing and diagnostic classifiers (Hupkes et ...the models ex- hibit activation curves consistent ...
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Text Generation using Neural Models
... Abstract: The use of automatically generated summaries for long/short texts is commonly used in digital services. In this Paper, a successful approach at text generation using generative adversarial networks (GAN) has ...
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Neural Models for Documents with Metadata
... We have presented a neural framework for general- ized topic models to enable flexible incorporation of metadata with a variety of options. We take advantage of stochastic variational inference to de- velop ...
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Deep Neural Models of Semantic Shift
... Although this evaluation provides useful infor- mation on the quality of an diachronic distribu- tional model, it has some weaknesses. The first is that it is a synthetic task that operates on synthetic words. Thus, we ...
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A Comparison of Neural Models for Word Ordering
... Beam search is a popular decoding algorithm for neural sequence models (Sutskever et al., 2014; Bahdanau et al., 2015). However, standard beam search suffers from search errors when applied to word ordering ...
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On the Memory Properties of Recurrent Neural Models
... typical neural cells that integrate their inputs and apply a non-linear activation function (typically a logistic or tanh sigmoid), with units that add a perfect integrator (referred to as cell state), between the ...
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Inducing Neural Models of Script Knowledge
... cessing ensured that (1) clues which form part of a pattern are not observable by our model both at train and test time; (2) there is no systematic dif- ference between both events (e.g., for collapsed dependencies, the ...
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Neural Models of Factuality
... our models need to be able to capture in order to correctly predict the factuality of an event denoted by a particular predicate—namely, interactions be- tween that predicate’s outside and inside context, ...
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Neural Temporal Relation Extraction
... best-performing neural model was the CNN with only tokens as fea- ...our neural mod- els except the token-based LSTM outperformed the state-of-the-art THYME system, and all mod- els performed as well or ...
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A Neural Network Approach to Verb Phrase Ellipsis Resolution
... each neural model, there are 6 experimental settings for VPE resolution. Accuracy, precision (P), recall (R) and F1 score (F) are used for evaluation. Table 4 shows the VPE reso- lution results on accuracy ...
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Benchmarking Hierarchical Script Knowledge
... sequence-to-sequence models with attention (Bahdanau et ...that models that learn explicit alignments are particularly amenable to interpretable analysis on the ...standard neural models, this ...
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The JHU Machine Translation Systems for WMT 2016
... Preliminary results with this approach were in- conclusive. For example, on the Russian-English newstest2015, the BLEU score is 27.27 for 1-best vs. 27.31 for reranking. On German-English new- stest2015, the BLEU score ...
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Neural Network Models of Categorical Perception
... inputs) neural models of CP have no real problem replicating the classical observations of a sharp labeling function and a peaked discrimination func- tion, at least for learned ...the neural ...
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Ensembles of Neural Morphological Inflection Models
... component models in the en- semble using both random initialization of model parameters and varying the training data using bootstrap aggregation commonly known as bag- ging (Breiman, ...for neural ...
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Models and Neural Bases of the Believing Process
... neuroscience models of the believing process and propose a conceptual framework that integrates current theoretical and empiric knowledge about the processes of be- ...dual-component models assume a belief ...
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Citation Analysis with Neural Attention Models
... The neural models were trained only on the training set while SVM model was built on both training and development ...the neural models for each epoch to choose the best ...the models ...
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Generalizing and Hybridizing Count based and Neural Language Models
... with neural network ...into neural language models, which allows for more direct learning of n- gram weights (Mikolov et ...and neural models, finding that neural models ...
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