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After a lot of consideration, the general chairs, the Executive Committee and the Organizing Committee for KDD 2020 decided to completely seize the conference virtually. The events of the last months and the persistent security concerns have brought us to make this difficult decision. Our focus is now to create a virtual conference with the Vibrancy, Excellence and the sense of community for which KDD has become known.
In the coming weeks, the agenda and the more programmatic details are announced, the website and our social media posts. Our plans include all tracks and content on which they came to KDD, and five exciting keynote speakers! The registration is now open, delete your calendar for the 23rd and 20th August, 2020 and enjoy access to all virtual content live and on request the week of the event.
We look forward to seeing you next year when we want to be in a safe way. We also pursue a KDD 2023 conference in San Diego, so we can recognize the hard work. This committee has installed and show the KDD community this beautiful place!
Until then, please disperse the word and feel free to write us with all questions or virtual best practices that you want to share.
Rajesh Gupta and Yan Liu
KDD 2020 General Chairs
Muhammad Abu Bakar Siddique: University of California, Riverside; Samet Oymak: University of California, Riverside; Vagelis Hristidis: University of California, RiversideParaphrasing expresses the meaning of an input rate set in different words, while fluent (i.e., grammatic and syntactical accuracy) is waiting. Most existing work on paraphrasing monitored models that are limited to specific domains (eg image signatures). Such models can not be easily transmitted well easily to other domains, and the creation of selected training data for new domains is expensive and tedious. The need to invoke different domains and the scarcity of the marked training data in many such domains call to explore unsexced paraphras generation methods. We suggest progressive unattended paraphrasing (puppy): a novel, unattended paraphrave generation method based on deep reinforcing learning (DRL). The puppy uses a variation autocoder (trained with a non-parallel corpus) to produce a seed paraphrase that starts the DRL model. Then, the puppy progressively agrees with the seed paraphrases directed by our novel rewarding function that combines semantic adequacy, voice fluid and expression variety measures to quantify the quality of the paraphrases produced in any iteration without needing parallel phrases. Our extensive experimental evaluation shows that the puppy overrides automatic metrics and user studies on four real data records unrestricted modern environmental techniques. We also show that the puppy exaggerates in several records domain adapted monitored algorithms. Our evaluation also shows that the puppy achieves a large compromise between semantic resemblance and expressiveness.
We will update the website because information is available. If you have a question that requires immediate attention, please contact us. Thanks!
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