[PDF] Top 20 Dependency Recurrent Neural Language Models for Sentence Completion
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Dependency Recurrent Neural Language Models for Sentence Completion
... Each sentence consists of one ground truth and 4 impostor sentences where a specific word has been replaced with a syntactically cor- rect but semantically incorrect impostor ... See full document
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Dependency Recurrent Neural Language Models for Sentence Completion
... Most neural language models consider the to- kens in a sentence in the order they appear, and the hidden state representation of the network is typically reset at the beginning of each sen- ... See full document
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Incorporating Side Information into Recurrent Neural Network Language Models
... kinds of side information, including keywords, ti- tle, description. We attempted to inject those into different RNNLM layers, resulting in model vari- ants as shown in Table 2. First, we chose “key- words” (+k) ... See full document
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Online Representation Learning in Recurrent Neural Language Models
... Model performance is measured using perplex- ity, therefore lower values indicate a model which is able to better predict the data. Special tokens are used to mark the beginning and end of a sen- tence. The ... See full document
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Recurrent Neural Network Based Sentence Encoder with Gated Attention for Natural Language Inference
... natural language understanding models for sentence representation, in which a sentence is represented as a fixed- length vector with neural networks and the quality of the ... See full document
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Personalized neural language models for real world query auto completion
... Recurrent Neural Network The difficulty of predicting queries given a prefix is that the number of candidates explodes as the query becomes ...gated recurrent units (GRU) (Cho et ... See full document
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A Latent Variable Recurrent Neural Network for Discourse Driven Language Models
... current neural network architecture for jointly modeling sequences of words and (possibly latent) discourse relations between adjacent ...A recurrent neural network gen- erates individual words, thus ... See full document
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Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking
... The use of neural network-based approaches to NLG is relatively unexplored. The stock reporter system ANA by Kukich (1987) is a network based NLG system, in which the generation task is di- vided into a ... See full document
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An Analysis of the Ability of Statistical Language Models to Capture the Structural Properties of Language
... n-gram models in modeling sentence ...the recurrent layer seems to exceed its apparent depth during training, taking advantage of the ability of re- current memory to retain subtle contextual ... See full document
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Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding
... discriminative models such as CRFs and sequence neural networks have been widely explored for spoken language ...instance, Recurrent Neural Networks have been pro- posed in (Yao et ... See full document
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Sparse Non negative Matrix Language Modeling
... for language modeling that can efficiently incorporate arbitrary ...SNM language models on two cor- pora: the One Billion Word Benchmark and a subset of the LDC English Gigaword cor- ...SNM ... See full document
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Decoder Integration and Expected BLEU Training for Recurrent Neural Network Language Models
... All neural network models are trained on the news portion of the parallel data, corresponding to 136K sentences, which we found to be most useful in initial exper- ...the language model but find that ... See full document
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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
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Dependency Language Models for Sentence Completion
... complete sentence is given and the candidate must choose the word or phrase from a list of options which completes the sentence in a logically consistent ...tence completion is a challenging semantic ... See full document
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Unsupervised Recurrent Neural Network Grammars
... improve language models with supervision on parsed ...underperforms models that explic- itly take into account structure (RNNG/URNNG) when the sentence length is greater than ...1M ... See full document
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Combination of Recurrent Neural Networks and Factored Language Models for Code Switching Language Modeling
... For this work, three different topics are investi- gated and combined: linguistic investigation of Code-Switching, recurrent neural network lan- guage modeling and factored language models. In ... See full document
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Dependency based Convolutional Neural Networks for Sentence Embedding
... The TREC dataset also provides subcategories such as numeric:temperature, numeric:distance, and entity:vehicle. To make our task more real- istic and challenging, we also test the proposed model with respect to the 50 ... See full document
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Enhancing recurrent neural network-based language models by word tokenization
... the language models ...[4]. Neural network-based language models offer several ...n-gram language models, smoothing must be handled explicitly for an unseen ...[5]. ... See full document
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Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
... our models to the recently proposed standard Neu- ral Machine Translation (NMT) ...NMT models and report numbers in the same ball- park as RAS-Elman on both ... See full document
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Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
... topic models, models that simultaneously analyze a collection of texts and its corresponding user ...topic models on 80,000 scientists’ libraries from Mendeley and 100,000 users’ click data from the ... See full document
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