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[PDF] Top 20 Compression in Sequence to Sequence Learning for Natural Language Processing

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Compression in Sequence to Sequence Learning for Natural Language Processing

Compression in Sequence to Sequence Learning for Natural Language Processing

... made in recent years in the field of general purpose sentence ...serving for a wide range of downstream ...AllNLI natural language inference corpus [Bowman et ...and natural ... Voir le document complet

62

Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labelling

Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labelling

... is to show that even in this situation (equivalent test performance), the ℓ 1 regularization may be preferable as sparsity in the parameter set can be exploited to reduce the computational ... Voir le document complet

22

Deep neural networks for natural language processing and its acceleration

Deep neural networks for natural language processing and its acceleration

... Generally, in these models, the correctness of the output tree is not strictly ensured (although empirically ...operating in a transition-based setting [23] by parsing either in the top-down ... Voir le document complet

140

Examining citations of natural language processing literature

Examining citations of natural language processing literature

... A. In this and all further analyses, we do not include AA 0 papers published in 2017 or later (to allow for at least ...years for the papers to collect ...1965 to 2016. ... Voir le document complet

12

Token-level and sequence-level loss smoothing for RNN language models

Token-level and sequence-level loss smoothing for RNN language models

... setup. For this task we validate the effectiveness of our approaches on two different ...English to French, in its filtered version, with 12M sentence pairs obtained after dynami- cally selecting a ... Voir le document complet

17

Recurrent neural models and related problems in natural language processing

Recurrent neural models and related problems in natural language processing

... discussed in the begin- ning of this section. Before advanced machine learning approaches become popular, MRC did not attract much attention from AI researchers and there were only a few attempts towards ... Voir le document complet

161

Comparison of natural language processing algorithms for medical texts

Comparison of natural language processing algorithms for medical texts

... batch processing, one aspect that can be improved upon is a better method of parallelizing the processing for the different ...slow to run multiple annotators at ...according to the ... Voir le document complet

58

Cerebral correlates of explicit sequence learning

Cerebral correlates of explicit sequence learning

... linked to either process therefore remain ...tended to use absolute measures of ...Indeed, in the absence of a clear operational criterion for awareness, it appears premature to ... Voir le document complet

10

Principles of Evaluation in Natural Language Processing

Principles of Evaluation in Natural Language Processing

... asked to identify bottlenecks, barriers and limits of the ...methods to better understand the acceptance or the refusal of ICT systems by ...framework for "user-centered" evaluation, even if ... Voir le document complet

26

Processing of contextual information during an implicit probabilistic sequence learning task: Left ventrolateral prefrontal cortex involvement

Processing of contextual information during an implicit probabilistic sequence learning task: Left ventrolateral prefrontal cortex involvement

... RTs for G and NG stimuli revealed a significant effect of the interaction between grammaticality and global RT in LI context, in accordance with previous studies’”, suggesting that with restricted ... Voir le document complet

2

Natural Language Processing for virtual assistants: what contribution synthetic data could bring to intents classification?

Natural Language Processing for virtual assistants: what contribution synthetic data could bring to intents classification?

... way to isolate the most difficult intents to classify, we should not forget that our classification program is very simple and only based on statistical occurrences of terms most commonly associated with ... Voir le document complet

98

Gender gap in natural language processing research: disparities in authorship and citations

Gender gap in natural language processing research: disparities in authorship and citations

... Anthology to show that only ∼30% have female authors, ∼29% have female first authors, and ∼25% have female last ...made in the early years of NLP, overall FFA% has not im- proved since the mid ...close ... Voir le document complet

13

Editorial: Special issue on natural language processing and text analytics in industry

Editorial: Special issue on natural language processing and text analytics in industry

... system for analyzing survey responses expressed in English and German. In essence, their analysis involves determining the sentiments of responses and clustering them according to ... Voir le document complet

2

Insights for Configuration in Natural Language

Insights for Configuration in Natural Language

... the Natural Language Understat- ing (NLU) [4] and Natural Language Processing (NLP) [12] draw at- tention for its capabilities of human computer ...interaction. In ... Voir le document complet

5

Brain plasticity related to the consolidation of motor sequence learning and motor adaptation

Brain plasticity related to the consolidation of motor sequence learning and motor adaptation

... motor learning processes dependent on sleep for consolidation to occur, whereas others are not? One possible answer to that question relies on the difference in the acquisition ... Voir le document complet

6

Empirical study and multi-task learning exploration for neural sequence labeling models

Empirical study and multi-task learning exploration for neural sequence labeling models

... jointly learning two tasks, often with one being considered as the main task, the other being the auxiliary one [56, 6, ...3]. For instance, chunking, combinatory categorical grammar supertagging, NER, ... Voir le document complet

83

Weakly supervised discriminative training of linear models for Natural Language Processing

Weakly supervised discriminative training of linear models for Natural Language Processing

... Recognition For task 2, experiments are realized both with the closed- form risk estimation in ...integration. For numerical integration, we have made preliminary experiments with both the ... Voir le document complet

13

Special Issue on Natural Language Processing and Information Systems

Special Issue on Natural Language Processing and Information Systems

... contributions to the conference (12 full papers, 24 short papers, and 17 poster presentations) were asked to develop their paper into a journal ...selected for this special issue. ‘A semi supervised ... Voir le document complet

1

Sequence-to-sequence learning for machine translation and automatic differentiation for machine learning software tools

Sequence-to-sequence learning for machine translation and automatic differentiation for machine learning software tools

... our language to be purely functional therefore al- lows us to implement more robust AD and more advanced optimizations compared to imperative ...Similarly to, ...derivatives in a ... Voir le document complet

180

Temporal Logic in Natural Language Processing

Temporal Logic in Natural Language Processing

... research in the field. 7.1 The work of the group Human Language Technology Research Institute (HLTRI) on temporal inference: HLTRI is a research group working on temporal ...of language (TimeML1) ... Voir le document complet

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