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Augmenting Modelers with Semantic Autocompletion of Processes [article]

Maayan Goldstein, Cecilia Gonzalez-Alvarez
2021 arXiv   pre-print
In this paper, we present a method for process autocompletion at design time, that is based on the semantic similarity of sub-processes.  ...  Business process modelers need to have expertise and knowledge of the domain that may not always be available to them.  ...  We used a state-of-the-art NLP technique that detects similarities between sentences and adopted it to the domain of business processes models.  ... 
arXiv:2105.11385v1 fatcat:3j5n7xna5fabjcsyx4cjqsfixu

Improving Code Autocompletion with Transfer Learning [article]

Wen Zhou, Seohyun Kim, Vijayaraghavan Murali, Gareth Ari Aye
2021 arXiv   pre-print
But what if limited examples of IDE autocompletion in the target programming language are available for model training?  ...  We confirm the real-world impact of these pretrainings in an online setting through A/B testing on thousands of IDE autocompletion users, finding that pretraining is responsible for increases of up to  ...  Furthermore, could the pretrained model for another task be used effectively as the base model for autocompletion as well? VII.  ... 
arXiv:2105.05991v2 fatcat:upytxisexrajjioyppmbtx3rfa

A Lightweight API-Based Approach for Building Flexible Clinical NLP Systems

Zhengru Shen, Hugo van Krimpen, Marco Spruit
2019 Journal of Healthcare Engineering  
Natural language processing (NLP) has become essential for secondary use of clinical data. Over the last two decades, many clinical NLP systems were developed in both academia and industry.  ...  The development and evaluation of the prototype demonstrates that our approach has a great potential for building effective clinical NLP systems with limited resources.  ...  Acknowledgments is work is part of the project "OPERAM: OPtimising thERapy to prevent Avoidable hospital admissions in the  ... 
doi:10.1155/2019/3435609 pmid:31511785 pmcid:PMC6714318 fatcat:p4ofmq7oabbk5cqneh4ttiotny

A Survey of Machine Learning for Big Code and Naturalness

Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, Charles Sutton
2018 ACM Computing Surveys  
We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature.  ...  Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit  ...  This context-based model captures all usages of an object and models the probability distribution for the next call.  ... 
doi:10.1145/3212695 fatcat:iuuocyctg5adjmobhc2zw23rfu

Editing Knowledge in Large Mathematical Corpora. A case study with Semantic LaTeX (sTeX) [article]

Constantin Jucovschi
2010 arXiv   pre-print
The result of the current research is that, indeed, IDEs can be very useful in the process of formalization and presents a set of best practices for implementing such IDEs.  ...  A list of research questions is compiled along with a set of software requirements which are then used for developing a new IDE for the semantic () format.  ...  I want to thank him for the great deal of support and understanding he showed when it came to more personal matters. I could not be happier to have Prof. Dr.  ... 
arXiv:1010.5935v1 fatcat:3tsdfsy4grgwrda7gjmy2kywam

Natural Language-Guided Programming [article]

Geert Heyman, Rafael Huysegems, Pascal Justen, Tom Van Cutsem
2021 arXiv   pre-print
Central to the tool is the use of language models trained on a large corpus of documented code.  ...  We put forward a vision based on a new breed of developer tools that have the potential to largely automate this process.  ...  and labeling the data used in our experiments.  ... 
arXiv:2108.05198v2 fatcat:3gayryvr2jb27bxpfz3h2hgdd4

A Survey of Machine Learning for Big Code and Naturalness [article]

Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, Charles Sutton
2018 arXiv   pre-print
We present a taxonomy based on the underlying design principles of each model and use it to navigate the literature.  ...  Research at the intersection of machine learning, programming languages, and software engineering has recently taken important steps in proposing learnable probabilistic models of source code that exploit  ...  This context-based model captures all usages of an object and models the probability distribution for the next call.  ... 
arXiv:1709.06182v2 fatcat:hbvgyonqsjgq3nqwji6jf3aybe

Natural language processing for web browsing analytics: Challenges, lessons learned, and opportunities

Daniel Perdices, Javier Ramos, José L. García-Dorado, Iván González, Jorge E. López de Vergara
2021 Computer Networks  
However, sniffers based on HTTP, DNS, TLS or flow features do not suffice for this task.  ...  Moreover, DNS caches prevent some queries of actively visited websites to be even sent. On this limited input, we propose to handle such domains as words and the sequences of domains as documents.  ...  Universities under the program for the training of university lecturers (Grant number: FPU19/05678).  ... 
doi:10.1016/j.comnet.2021.108357 fatcat:n7kjsmtmkrgvdcxpl3u7isvsem

Predictive translation memory

Spence Green, Jason Chuang, Jeffrey Heer, Christopher D. Manning
2014 Proceedings of the 27th annual ACM symposium on User interface software and technology - UIST '14  
We present Predictive Translation Memory, an interactive, mixed-initiative system for human language translation.  ...  Translators build translations incrementally by considering machine suggestions that update according to the user's current partial translation.  ...  Any opinions, findings, and conclusions or recommendations expressed are those of the author(s) and do not necessarily reflect the view of either DARPA or the US government.  ... 
doi:10.1145/2642918.2647408 dblp:conf/uist/GreenCHM14 fatcat:b6xfbycuibhpbkmeteu43744by

A Reinforcement Learning Approach to Interactive-Predictive Neural Machine Translation [article]

Tsz Kin Lam, Julia Kreutzer, Stefan Riezler
2018 arXiv   pre-print
reduced to an average number of 5 feedback requests for every input.  ...  Lastly, online updates of the model parameters after every interaction allow the model to adapt quickly.  ...  We would like to thank the members of the Statistical NLP Colloquium Heidelberg for fruitful discussions and ideas for improvement of our algorithm.  ... 
arXiv:1805.01553v3 fatcat:b5brpvxpu5fdlfuxhnxikibtyq

Designing for Health Chatbots [article]

Ahmed Fadhil, Gianluca Schiavo
2019 arXiv   pre-print
In this chapter, we introduce the nature of conversational user interfaces (CUIs) for health and describe UX design principles informed by a systematic literature review of relevant research works.  ...  Other more complex elements include using emotionally intelligent conversational agent to build trust with the individuals.  ...  The work contributes to the rise of fluency level of XML chatterbots by providing a linguistically grounded model for chatterbots' markup languages and allowing the extension of existing bases to different  ... 
arXiv:1902.09022v1 fatcat:hn4phntq6bbu5oylefx3zmck2i

A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images [article]

Pablo Messina, Pablo Pino, Denis Parra, Alvaro Soto, Cecilia Besa, Sergio Uribe, Marcelo andía, Cristian Tejos, Claudia Prieto, Daniel Capurro
2022 arXiv   pre-print
Every year physicians face an increasing demand of image-based diagnosis from patients, a problem that can be addressed with recent artificial intelligence methods.  ...  In this context, we survey works in the area of automatic report generation from medical images, with emphasis on methods using deep neural networks, with respect to: (1) Datasets, (2) Architecture Design  ...  The authors manually designed an abnormality graph and a disease graph, where each node represents an abnormality or disease, and the edges are built based on their co-occurrences in the training set.  ... 
arXiv:2010.10563v2 fatcat:usmbthlgorevliiyw7llox6zky

ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction [article]

Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christopher Potts, Matei Zaharia
2021 arXiv   pre-print
We evaluate ColBERTv2 across a wide range of benchmarks, establishing state-of-the-art quality within and outside the training domain while reducing the space footprint of late interaction models by 5–  ...  This decomposition has been shown to make late interaction more effective, but it inflates the space footprint of these models by an order of magnitude.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.  ... 
arXiv:2112.01488v2 fatcat:vaucpjvuejbidiibss5pxgu7y4

PARTHENOS D6.4 Report on services and tools (final)

Alessia Bardi, George Bruseker, Matej Durco, Klaus Illmayer, Daan Broeder, Matteo Lorenzini, Stefan Resch, Go Sugimoto, Maria Theodoridou, Massimiliano Assante
2019 Zenodo  
This document reports on the final status of actions carried out towards achieving WP6 objectives, especially with regards to tools and services enabling interoperability, sharing specialized tools and  ...  them into an efficient web format, making them ready for web-based visualisation.  ...  The Visual Media service will make accessible to users an easy-to-use way for publishing advanced multimedia content on the Web, to upload visual media files on a server and to automatically transform  ... 
doi:10.5281/zenodo.2607211 fatcat:p2tf5jxchjgwvdhengzmu53nrq

What do Bias Measures Measure? [article]

Sunipa Dev, Emily Sheng, Jieyu Zhao, Jiao Sun, Yu Hou, Mattie Sanseverino, Jiin Kim, Nanyun Peng, Kai-Wei Chang
2021 arXiv   pre-print
To address this gap, this work presents a comprehensive survey of existing bias measures in NLP as a function of the associated NLP tasks, metrics, datasets, and social biases and corresponding harms.  ...  Natural Language Processing (NLP) models propagate social biases about protected attributes such as gender, race, and nationality.  ...  Autocomplete Generation Autocomplete generation is the task of having a language model generate continuations from a prompt. Sheng et al.  ... 
arXiv:2108.03362v1 fatcat:migucaqmgne3hiyirl755o4dpu
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