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A Primal decomposition algorithm for distributed multistage scenario model predictive control
2019
Journal of Process Control
Preserved Fulltext
This paper proposes a primal decomposition algorithm for efficient computation of multistage scenario model predictive control, where the future evolution of uncertainty is represented by a scenario tree ...
The performance of the proposed approach, and the backtracking algorithm is demonstrated using a CSTR case study. ...
In recent years, there has been an increasing trend in the use of economic objectives in the framework of nonlinear model predictive control, known as economic MPC. ...
doi:10.1016/j.jprocont.2019.02.003
fatcat:6lmz35r2ubgrlj3fnnpny6mgzu
Emoji Prediction: Extensions and Benchmarking
[article]
2020
arXiv
pre-print
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Through emoji prediction, models can learn rich representations of the communicative intent of the written text. ...
Our results demonstrate the efficacy of deep Transformer-based models on the emoji prediction task. ...
With the extended emoji-set, we observe much more potential of emoji prediction models in the NLP field. ...
arXiv:2007.07389v1
fatcat:m4qlf2lzxveuvcwapxqzqhy6l4
Neural models of factuality
[article]
2018
arXiv
pre-print
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We also present a substantial expansion of the It Happened portion of the Universal Decompositional Semantics dataset, yielding the largest event factuality dataset to date. ...
We present two neural models for event factuality prediction, which yield significant performance gains over previous models on three event factuality datasets: FactBank, UW, and MEANTIME. ...
The views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsements of DARPA or the U.S. Government. ...
arXiv:1804.02472v1
fatcat:hisryuisk5ar7hmzewmn7jrocy
Neural Models of Factuality
2018
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
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We also present a substantial expansion of the It Happened portion of the Universal Decompositional Semantics dataset, yielding the largest event factuality dataset to date. ...
We present two neural models for event factuality prediction, which yield significant performance gains over previous models on three event factuality datasets: FactBank, UW, and MEANTIME. ...
The views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsements of DARPA or the U.S. Government. ...
doi:10.18653/v1/n18-1067
dblp:conf/naacl/RudingerWD18
fatcat:nwxl2q6vmrcmvpt2vr5j2sj5ze
Beyond LDA: Exploring Supervised Topic Modeling for Depression-Related Language in Twitter
2015
Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality
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In this paper, we explore the use of supervised topic models in the analysis of linguistic signal for detecting depression, providing promising results using several models. ...
Topic models can yield insight into how depressed and non-depressed individuals use language differently. ...
Any opinions, findings, conclusions, or recommendations expressed here are those of the authors and do not necessarily reflect the view of the sponsor. ...
doi:10.3115/v1/w15-1212
dblp:conf/naacl/ResnikACNNB15
fatcat:ipiivoywlzainfxmcou7fwvwwi
Prediction of black box warning by mining patterns of Convergent Focus Shift in clinical trial study populations using linked public data
2016
Journal of Biomedical Informatics
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We also demonstrated the feasibility of the predictor for identifying long-term BBW acquisition events without compromising prediction accuracy. ...
A random forest predictive model was developed to predict BBW acquisition incidents based on CFS patterns among these drugs. ...
This result shows that given enough data for model training, the model could predict
the future BBW acquisition events quite well, with little accuracy loss even for long-term
predictions. ...
doi:10.1016/j.jbi.2016.01.015
pmid:26851401
pmcid:PMC4837034
fatcat:bw5isufaxzff3lkjn4pxxrun5a
Confidence Modeling for Neural Semantic Parsing
[article]
2018
arXiv
pre-print
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Beyond confidence estimation, we identify which parts of the input contribute to uncertain predictions allowing users to interpret their model, and verify or refine its input. ...
Experimental results show that our confidence model significantly outperforms a widely used method that relies on posterior probability, and improves the quality of interpretation compared to simply relying ...
Acknowledgments We would like to thank Pengcheng Yin for sharing with us the preprocessed version of the DJANGO dataset. ...
arXiv:1805.04604v1
fatcat:tmhxcmf6avcabm3rrd4beyewzq
Confidence Modeling for Neural Semantic Parsing
2018
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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Beyond confidence estimation, we identify which parts of the input contribute to uncertain predictions allowing users to interpret their model, and verify or refine its input. ...
Experimental results show that our confidence model significantly outperforms a widely used method that relies on posterior probability, and improves the quality of interpretation compared to simply relying ...
Acknowledgments We would like to thank Pengcheng Yin for sharing with us the preprocessed version of the DJANGO dataset. ...
doi:10.18653/v1/p18-1069
dblp:conf/acl/QuirkLD18
fatcat:c764d4i36ba57gdqqwzgtcwiuu
Neural Models for Detecting Binary Semantic Textual Similarity for Algerian and MSA
2019
Proceedings of the Fourth Arabic Natural Language Processing Workshop
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We compare the performance of various models on both datasets and report the best performing configurations. ...
The results show that relatively simple models composed of 2 LSTM layers outperform by far other more sophisticated attention-based architectures, for both ALG and MSA datasets. ...
in Probability (CLASP) at the University of Gothenburg. ...
doi:10.18653/v1/w19-4609
dblp:conf/wanlp/AdouaneBD19
fatcat:cribkn53bnfs3j4wo575qar5nq
RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2009.11462v1 fatcat:ioyipmbqlfaj5c4stlo5r44fna
We investigate the extent to which pretrained LMs can be prompted to generate toxic language, and the effectiveness of controllable text generation algorithms at preventing such toxic degeneration. ...
Pretrained neural language models (LMs) are prone to generating racist, sexist, or otherwise toxic language which hinders their safe deployment. ...
neural language models, and therefore use the term "neural toxic degeneration." ...
arXiv:2009.11462v2
fatcat:sdzqn6oumjgwvheetr2jrgggqq
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
[article]
2016
arXiv
pre-print
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In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art ...
We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. ...
Banchs for providing the Movie-DiC dataset, and Luisa Coheur for providing the SubTle dataset. The authors also thank the anonymous AAAI reviewers for their helpful feedback. ...
arXiv:1507.04808v3
fatcat:sw2dgffakvchphom64e2ebg43y
A Survey of Machine Narrative Reading Comprehension Assessments
[article]
2022
arXiv
pre-print
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As the body of research on machine narrative comprehension grows, there is a critical need for consideration of performance assessment strategies as well as the depth and scope of different benchmark tasks ...
differences among assessment tasks; and discuss the implications of our typology for new task design and the challenges of narrative reading comprehension. ...
Acknowledgements This research was supported, in part, by the NSF (USA) under Grant Numbers CNS-1948457. ...
arXiv:2205.00299v1
fatcat:ueifos3ymrhhfpd7hkbzdagxwy
On the Opportunities and Risks of Foundation Models
[article]
2021
arXiv
pre-print
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Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. ...
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. ...
Acknowledgments References
ACKNOWLEDGMENTS We would like to thank the following people for their valuable feedback: Mohit Bansal, Boaz Barak, Yoshua Bengio, Sam Bowman, Collin Burns, Nicholas Carlini ...
arXiv:2108.07258v2
fatcat:yktkv4diyrgzzfzqlpvaiabc2m
Contextualization of Morphological Inflection
2019
Proceedings of the 2019 Conference of the North
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We experiment on several typologically diverse languages from the Universal Dependencies treebanks, showing the utility of incorporating linguisticallymotivated latent variables into NLP models. ...
In this paper, we isolate the task of predicting a fully inflected sentence from its partially lemmatized version. ...
The first author would like to acknowledge the Google PhD fellowship. The second author would like to acknowledge a Facebook Fellowship. ...
doi:10.18653/v1/n19-1203
dblp:conf/naacl/VylomovaCCBE19
fatcat:psy3hsja3ne4xbbow67gyewgou
Contextualization of Morphological Inflection
[article]
2019
arXiv
pre-print
Preserved Fulltext
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The file type is application/pdf.
We experiment on several typologically diverse languages from the Universal Dependencies treebanks, showing the utility of incorporating linguistically-motivated latent variables into NLP models. ...
In this paper, we isolate the task of predicting a fully inflected sentence from its partially lemmatized version. ...
The first author would like to acknowledge the Google PhD fellowship. The second author would like to acknowledge a Facebook Fellowship. ...
arXiv:1905.01420v1
fatcat:chzwtudvw5fzbdrarli2xktade
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