[PDF] Top 20 Dynamic Entity Representations in Neural Language Models
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Dynamic Entity Representations in Neural Language Models
... current entity; no long-term history of the entity is maintained, just the current local ...that entity infor- mation is provided at test time (Yang, personal communication), which makes a direct ... See full document
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Building Compact Entity Embeddings Using Wikidata
... natural language text suggest that a Natural Language Processing (NLP) model is always expected to encounter new word sequences that have never been seen during the building of the ...Statistical ... See full document
9
Entity Decisions in Neural Language Modelling: Approaches and Problems
... explicit entity information is helpful in a general language modelling task, and the models’ abilities especially to predict where to form entities have shown to be limited, we see a potential for ... See full document
5
The JHU Machine Translation Systems for WMT 2016
... The neural probablistic language model (NPLM) was proposed by Bengio et ...traditional language models with a feed forward neural ...continuous representations known as word ... See full document
9
Linking artificial and human neural representations of language
... within language neuroscience and ...describe language understanding behavior are lin- early decodable from fMRI ...human language understanding and those deployed within artificial neural ... See full document
11
A Trio Neural Model for Dynamic Entity Relatedness Ranking
... relatedness models that are optimized for ranking systems such as top-k entity retrieval or ...traditional entity ranking (Kang et al., 2015) in that the entity rankings are driven by user ... See full document
11
Neural Text Generation in Stories Using Entity Representations as Context
... the entity-aware ...offers language that is much closer to our intended narrative text generation ...our models’ intrinsic cor- rectness, though we emphasize that even if entity information is ... See full document
11
Correlating Neural and Symbolic Representations of Language
... learning models in NLP has brought an increasing interest in techniques to analyze these models and gain insight into how they encode linguistic ...diagnostic models, which use the internal ... See full document
11
Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction
... state-of-the-art neural models and Weston over the entire range of ...the representations and biased toward ...erogeneous representations bring mutual benefits which are out of reach of ... See full document
6
Multilingual Language Models for Named Entity Recognition in German and English
... With the rise of recurrent neural networks (RNNs) in NLP, they became a better choice for (b) the Encoding layer of the LM (Mikolov et al., 2010). Especially the variation of a long-short- term memory (LSTM) RNN ... See full document
7
Building Context aware Clause Representations for Situation Entity Type Classification
... our neural network models on the training set of MASC+Wiki by treating each genre as one cross- validation ...paragraph-level models is little, which clearly outperform the pre- vious system ... See full document
11
Deep Neural Network Language Models
... deep neural network (DNN) with mul- tiple hidden layers can learn more higher-level, ab- stract representations of the ...using neural networks to process a raw pixel representation of an image, ... See full document
9
Convolutional Neural Network Language Models
... Convolutional Neural Networks (CNNs) have shown to yield very strong results in several Computer Vision ...to language has received much less attention, and it has mainly focused on static classifica- tion ... See full document
10
Data to text Generation with Entity Modeling
... of neural network architectures which are trained end-to-end. These models rely on represen- tation learning to select content appropriately, structure it coherently, and verbalize it gram- matically, ... See full document
13
How to Use Gazetteers for Entity Recognition with Neural Models
... end-to-end neural archi- tectures has been proven to be effective on several sequence labeling tasks, the use of gazetteers in these architectures is still rather ...a neural model for entity ... See full document
10
Neural language models as psycholinguistic subjects: Representations of syntactic state
... of neural network language models reflects incremental representations of syntactic ...amine neural network model behavior on sets of artificial sentences containing a variety of ... See full document
11
Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding
... Spoken Language Understanding (SLU), semantic decoding is usually seen as a sequence tagging problem with models trained and tested on datasets with word-level annotations (T¨ur et ...Spoken language ... See full document
10
Connecting Social Media to E-Commerce Site Using Cold Start Product Recommendation
... topic models assume individual words are exchangeable, which is essentially the same as the bag-of-words model ...Word representations or embeddings learned using neural language models ... See full document
8
Compressing Neural Language Models by Sparse Word Representations
... Time complexity. Training neural LMs is typi- cally time-consuming especially when the vocab- ulary size is large. The normalization factor in Equation (1) contributes most to time complex- ity. Morin and Bengio ... See full document
10
Polyglot Neural Language Models: A Case Study in Cross Lingual Phonetic Representation Learning
... polyglot neural language model (NLM) ...the language being predicted in each se- quence, but also on a vector representation of its phono-typological ...ing representations of phones as part ... See full document
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