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ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference [article]

Amit Gajbhiye, Noura Al Moubayed, Steven Bradley
2021 arXiv   pre-print
We introduce a new model for NLI called External Knowledge Enhanced BERT (ExBERT), to enrich the contextual representation with real-world commonsense knowledge from external knowledge sources and enhance  ...  Natural Language Inference (NLI) is a challenging reasoning task that relies on common human understanding of language and real-world commonsense knowledge.  ...  Conclusion We introduced ExBERT to enrich the contextual representation of BERT with real-world commonsense knowledge from external knowledge sources and to enhance its language understanding and reasoning  ... 
arXiv:2108.01589v1 fatcat:ccllx3bznvgfjhlop2mcimcgny

AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing [article]

Katikapalli Subramanyam Kalyan, Ajit Rajasekharan, Sivanesan Sangeetha
2021 arXiv   pre-print
Transformer-based pretrained language models (T-PTLMs) have achieved great success in almost every NLP task. The evolution of these models started with GPT and BERT.  ...  Transformed-based PTLMs learn universal language representations from large volumes of text data using self-supervised learning and transfer this knowledge to downstream tasks.  ...  ACKNOWLEDGMENTS Kalyan would like to thank his father Katikapalli Subramanyam for giving a) $750 to buy a new laptop, 24inch monitor and study table. b) $180 for one year subscription of Medium, Overleaf  ... 
arXiv:2108.05542v2 fatcat:4uyj6uut65d37hfi7yss2fek6q

The Grammar of Interactive Explanatory Model Analysis [article]

Hubert Baniecki, Dariusz Parzych, Przemyslaw Biecek
2022 arXiv   pre-print
The growing need for in-depth analysis of predictive models leads to a series of new methods for explaining their local and global properties. Which of these methods is the best?  ...  In contrast, we showcase the problem of explainability as an interactive and sequential analysis of a model.  ...  We also thank Anna Kozak for designing the graphics, Mateusz Krzyziński and Alicja Gosiewska for valuable discussions about this work, Kasia Woznica, Piotr Piatyszek, and Anna Kozak for providing expertise  ... 
arXiv:2005.00497v4 fatcat:aq3je4gx2faennfwh4x2l4phby

Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

Sebastian Raschka, Joshua Patterson, Corey Nolet
2020 Information  
Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and  ...  Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline.  ...  natural language understanding, speech comprehension, and image recognition.  ... 
doi:10.3390/info11040193 fatcat:hetp7ngcpbbcpkhdcyowuiiwxe

Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence [article]

Sebastian Raschka, Joshua Patterson, Corey Nolet
2020 arXiv   pre-print
Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and  ...  Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline.  ...  Acknowledgments: We would like to thank John Zedlewski, Dante Gama Dessavre, and Thejaswi Nanditale from the RAPIDS team at NVIDIA and Scott Sievert for helpful feedback on the manuscript.  ... 
arXiv:2002.04803v2 fatcat:lvbczmz7xvbyjhs65zubwluzb4

Benchmarking and Survey of Explanation Methods for Black Box Models [article]

Francesco Bodria, Fosca Giannotti, Riccardo Guidotti, Francesca Naretto, Dino Pedreschi, Salvatore Rinzivillo
2021 arXiv   pre-print
The widespread adoption of black-box models in Artificial Intelligence has enhanced the need for explanation methods to reveal how these obscure models reach specific decisions.  ...  Natural Language Explanation verbalizes explanations in natural human language.  ...  Natural language can be generated with complex deep learning models, e.g., by training a model with natural language explanations and coupling with a generative model [101] .  ... 
arXiv:2102.13076v1 fatcat:6iw6nhwltzb7fhaawnvy7oxndu