[PDF] Top 20 Dependency Based N Gram Models for General Purpose Sentence Realisation
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Dependency Based N Gram Models for General Purpose Sentence Realisation
... probabilistic models have become widely used in the field of natural lan- guage generation (NLG), often in the form of a re- alisation ranker in a two-stage generation architec- ...likely realisation from ... See full document
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Dependency Based Chinese Sentence Realization
... selection. Dependency Relation Model: For a particular sub-tree structure, the task of generating a string covered by the nodes on the sub-tree is equiva- lent to linearizing all the dependency relations in ... See full document
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Dependency Recurrent Neural Language Models for Sentence Completion
... language models with ...like n-grams, take into account the or- der of the words in the context and can thus model higher-order Markovian dynamics than the simple first-order autoregressive dynamics in ... See full document
7
Dependency Based Embeddings for Sentence Classification Tasks
... the dependency based skipgram of Levy and Goldberg (2014) which we further ex- tend in this ...model based on predicate-argument structures and report improvements on phrase similarity tasks compared ... See full document
11
Generalizing and Hybridizing Count based and Neural Language Models
... count-based n-grams on each of PTB, WSJ, and GW, and learning net parameters on only PTB ...Google n-grams (LDC2006T13), which contain n -gram counts but not full ... See full document
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Dependency Recurrent Neural Language Models for Sentence Completion
... language models with ...like n-grams, take into account the or- der of the words in the context and can thus model higher-order Markovian dynamics than the simple first-order autoregressive dynamics in ... See full document
7
From n gram based to CRF based Translation Models
... learned by optimizing some regularized loss func- tion of θ, so as to make the inferred input/output mapping faithfully replicate the observed instances. Machine translation, like most NLP tasks, does not easily lend ... See full document
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LIMSI@WMT’16: Machine Translation of News
... source sentence. Along with the n-gram translation models and target n- gram language models, 13 conventional features are combined: 4 lexicon models similar to the ... See full document
7
Letter N Gram based Input Encoding for Continuous Space Language Models
... ter n-grams will yield a good indicator for words that have the same function inside the ...letter n-gram approach to represent words in an CSLM, and compare it to the word-based CSLM ... See full document
10
Dependency Language Models for Sentence Completion
... tackle sentence completion us- ing language models based on dependency gram- ...These models are similar to standard n-gram language models, but instead of ... See full document
6
Sentence Realisation from Bag of Words with Dependency Constraints
... language models were trained on the words which were then used to compute the best strings associated with various ...Language models is that it deals with unseen words ...word based STLM is ap- ... See full document
6
Three Dependency and Boundary Models for Grammar Induction
... all sentence lengths of the evaluation sets are reported — attaining highest scores for 8 of 19 languages; the DMV baseline is still state-of-the- art for one language; and the remaining 10 bests are split among ... See full document
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General Purpose Media (BNO) for Growing Fastidious Gram Negative (FGN) Bacteria
... fastidious Gram negative bacteria (FGNs) (Table 1), their requirements for growth factors, and their relatively small colonies allow other organisms to outgrow ...including Gram positive, fungi, and ... See full document
12
Improvements to the Bayesian Topic N Gram Models
... trained models: an n-gram model p(w|h) and a topic model ...simpler models such as linear interpolation (Gildea and Hofmann, ...Bayesian models can rival the rescaling-based ... See full document
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Federated Learning of N Gram Language Models
... the forget and input decisions together, which re- duces the number of LSTM parameters by 25%. We also use group-LSTM (GLSTM) (Kuchaiev and Ginsburg, 2017) to reduce the number of train- able variables of an LSTM matrix ... See full document
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Faster and Smaller N Gram Language Models
... In a simple experiment, we recorded all of the language model queries issued by the Joshua de- coder (Li et al., 2009) on a 100 sentence test set. Of the 31 million queries, only about 1 million were unique. ... See full document
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Dependency based Convolutional Neural Networks for Sentence Embedding
... coded rules. We set batch size to 210 for this task. 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 ... See full document
6
An Empirical Evaluation of Stop Word Removal in Statistical Machine Translation
... After applying relaxed SMT, the resulting null tokens in the translated sentences have to be replaced by the corresponding words from the set of relaxed words. As relaxed words are chosen from the top ranked words, which ... See full document
8
LIMSI @ WMT’14 Medical Translation Task
... Looking at the most important types of errors, assuming the translation hypotheses were to be used for rapid assimilation of the text content, we find a moderate number of unknown terms and in- correctly translated ... See full document
8
Utilizing Dependency Language Models for Graph based Dependency Parsing Models
... develop models that represent first-order features over a single arc in ...These models utilize higher-order feature representations and achieve bet- ter performance than the first-order ...where n ... See full document
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