knowledge based neural models
Inducing Neural Models of Script Knowledge
... approach based on fre- quency of predicate pairs (Chambers and Jurafsky, 2008) (henceforth CJ08), is unlikely to make a right prediction as driving usually precedes disem- ...
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Bridging Knowledge Gaps in Neural Entailment via Symbolic Models
... various models on the SciTail ...baseline neural entail- ment ...external knowledge base, which are com- parable to the gains achieved by DGEM through the use of hypothesis ...recent models ...
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Leveraging Rule Based Machine Translation Knowledge for Under Resourced Neural Machine Translation Models
... feature-enriched models ob- tained slightly better results in terms of BLEU and TER compared to the baseline, no model obtains scores that are statistically significantly different than the baseline subword ...NMT ...
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Neural Character based Composition Models for Abuse Detection
... Our contributions are two-fold: first, we exper- imentally demonstrate that character n-gram fea- tures are complementary to the current state of the art RNN approaches to abusive language detection and can strengthen ...
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Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge
... resent our approach which uses the BERT lan- guage model. We compared our method with five other methods (two language models based and three others). The comparison results are as shown in the Table 2. The ...
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Neural Natural Language Inference Models Enhanced with External Knowledge
... state-of-the-art models on the SNLI ...namely Knowledge-based Inference Model (KIM), which enriches ESIM with external knowledge, obtains an accuracy of ...external knowledge, we add ...
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Using Morphological Knowledge in Open Vocabulary Neural Language Models
... language models that generate words from a fixed ...character-based models allow any possible word type to be generated, they are linguis- tically naïve: they must discover that words exist and are ...
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Artificial Intelligence Applications and Future Research Directions
... artificial neural networks (ANN) taxonomy and supplies investigators with current knowledge and raising needs in ANN based research applications and concentration for ...on. Based on this ...
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Random Walks and Neural Network Language Models on Knowledge Bases
... Turian et al., 2010). NNLM extract meaning from unlabeled corpora following the distributional hy- pothesis (Harris, 1954), where semantic features of a word are related to its co-occurrence patterns. NNLM learn word ...
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Enhancing Neural Data To Text Generation Models with External Background Knowledge
... 4.5 Analysis of Few-Shot Learning Ability To examine the ability of learning writing knowl- edge from few examples, we design an experiment to compare the performance under different num- ber of training samples for the ...
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Learning and Representing Temporal Knowledge in Recurrent Networks
... of knowledge representa- tion, reasoning and learning in a robust computational model is one of the key challenges of Computer Science and Artificial ...temporal knowledge and models have been ...
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Incorporating Word Reordering Knowledge into Attention based Neural Machine Translation
... The attention mechanism evaluates the dis- tribution of to-be-translated source words in a content-based addressing fashion (Graves et al., 2014) which tends to attend to the source words regarding the content ...
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Neural Network-based Models with Commonsense Knowledge for Machine Reading Comprehension
... deep neural networks. More specifically, they often use recurrent neural net- works (RNNs), a special type of networks, which process input ...NLP models, is the simultaneous process- ing of input ...
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Fuzzy Logic Model for the Prediction of Traffic Volume in Week Days
... forecasting models developed by researchers into two categories one is mathematical model and second is knowledge-based intelligent ...mathematical models include Historical average the Kalman ...
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Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge
... background knowledge (Section 4), based on reducing the generation problem to a combina- torial optimisation ...background knowledge into models by means of an adversar- ial training procedure ...
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Dynamic Explainable Recommendation Based on Neural Attentive Models
... promising models have been proposed in the recent ...mainly based themselves on the combination between matrix factorization (MF) and sentiment analysis ...these models may be limited by the accuracy ...
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Short-Term Forecast of Wind Speed through Mathematical Models
... models for forecasting time series applied in wind generation based on the combination of time series 828. models with artificial neural networks[r] ...
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The Assessment Models of Knowledge-Based Economy Penetration
... of knowledge economy policy is ...the knowledge, skills and other attributes of the workforce (OECD, 1996, ...of knowledge-based economy elements) have been pre- pared starting from 1962 ...of ...
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Knowledge Models, current Knowledge Acquisition Techniques and Developments
... the knowledge engineer who is only allowed to respond yes or ...the knowledge engineer notes this ...important knowledge such as key properties or categories in a prioritized ...
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Wheat Yield Prediction Using Artificial Neural Network and Crop Prediction Techniques (A Survey)
... analyzing models are practical instruments analyze plant reaction to climate changes ...processes based on regression relations are used to evaluate coefficients which relate plant reactions to climate ...
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