[PDF] Top 20 Compressing Neural Language Models by Sparse Word Representations
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Compressing Neural Language Models by Sparse Word Representations
... of sparse codes to represent word vec- tors in Embedding and the output weights in the Prediction ...a word is always companied by its ...a word or corresponding context are the same. As both ... See full document
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MulCode: A Multiplicative Multi way Model for Compressing Neural Language Model
... the word frequency: the most frequent words and the ...a sparse lin- ear combination of the vectors of more frequent ...some language model- ing tasks (Mitchell and Lapata, 2009, ... See full document
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Using Factored Word Representation in Neural Network Language Models
... as word factors, e.g. the lemma of word, POS tags, ...factored representations to smaller mapping steps, which are modelled by translation probabilities from input factor to out- put factor or by ... See full document
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Word Representations in Factored Neural Machine Translation
... a word in the base sentence and produces variants containing synonyms and antonyms of this ...two models introduced in § 6 (the inflected word system is our best system for each language ... See full document
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Sparse Bilingual Word Representations for Cross lingual Lexical Entailment
... learning representations that cap- ture context of words in two different languages in the ...of word con- texts observed in original language texts, as well as cross-lingual correspondences from ... See full document
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TüKaSt at SemEval 2019 Task 6: Something Old, Something Neu(ral): Traditional and Neural Approaches to Offensive Text Classification
... natural language processing is the prominent research on neural networks using dense vector representations (word embeddings) as ...for language modelling (Bojanowski et ...dense ... See full document
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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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Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding
... Convolutional Neural Networks (CNNs) have been used previously for sentiment analysis (Kim, 2014; Kalchbrenner et ...the models are initialised with GloVe word embeddings (Pennington et ... See full document
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Neural language models as psycholinguistic subjects: Representations of syntactic state
... the word-by-word surprisal values will show evidence for syntac- tic state ...a language model only if the model has a representation of a certain syn- tactic state going into the ...analyze ... See full document
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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 ...assumption. Word representations or embeddings learned using neural ... See full document
8
Using Priming to Uncover the Organization of Syntactic Representations in Neural Language Models
... We build on paradigms that use LM probability es- timates for words in a given context as a measure of the model’s sensitivity to the syntactic struc- ture of the sentence (Linzen et al., 2016; Gulor- dava et al., 2018; ... See full document
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Dynamic Entity Representations in Neural Language Models
... n-gram language model and a strong recurrent neural network language model on the English test set of the CoNLL 2012 shared task on coreference evaluation (Pradhan et ... See full document
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Neural Mechanisms for Combinatorial Semantics in Language and Vision: Evidence From FMRI, Patients, and Brain Stimulation
... the neural basis of semantic memory has examined the representation of semantic categories ...the neural regions that underlie conceptual combination. Many models of semantic memory have proposed ... See full document
147
Computational Ad Hominem Detection
... Mining ad hominem fallacies Habernal et al. (2018) discusses methods to detect name calling, a subset of ad hominem attacks. Their work is focussed on how and where these fallacies oc- cur in so-called discussion trees, ... See full document
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Simple task specific bilingual word embeddings
... bilingual word embeddings, i.e., word em- beddings such that similar words in two different languages end up close in the embedding ...bilingual word embeddings can potentially be used for better ... See full document
5
Improving Sparse Word Representations with Distributional Inference for Semantic Composition
... Distributional models are derived from co- occurrences in a corpus, where only a small proportion of all possible plausible co- occurrences will be ...very sparse vector space, requiring a mechanism for ... See full document
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Effectiveness of neural language models for word prediction of textual mammography reports
... natural language processing, we had implemented the ba- sic LSTM as described in PyTorch documentation, in- spired from Sherstinsky’s paper ...Natural Language Pro- cessing (NLPProgress) to reach maximum ... See full document
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Enhancing recurrent neural network-based language models by word tokenization
... any word, the word root. To find the word root, Khoja’s algorithm removes any prefix or suffix that is not considered as a part of a word root and compares the rest of the word to the ... See full document
13
Contextualized Word Representations for Reading Comprehension
... RC models has shown that models tend to react to simple word-matching be- tween the question and document (Jia and Liang, 2017), as well as benefit from explicitly provid- ing matching information in ... See full document
6
HIVEC: A Hierarchical Approach for Vector Representation Learning of Graphs
... vector representations of graphs in a manner that captures the semantic dependencies of ...The representations can then be used in machine learning algorithms for tasks such as graph classification, ... See full document
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