History-based Model Repair Recommendations

2021 ◽  
Vol 30 (2) ◽  
pp. 1-46
Author(s):  
Manuel Ohrndorf ◽  
Christopher Pietsch ◽  
Udo Kelter ◽  
Lars Grunske ◽  
Timo Kehrer
Keyword(s):  
Model Repair

Model Repair Revamped

2019 ◽  
pp. 107-125
Author(s):  
Milan Češka ◽  
Christian Dehnert ◽  
Nils Jansen ◽  
Sebastian Junges ◽  
Joost-Pieter Katoen
Keyword(s):  
Model Repair

scholarly journals Automated Conversation Review to Surface Virtual Assistant Misunderstandings: Reducing Cost and Increasing Privacy

2020 ◽  
Vol 34 (08) ◽  
pp. 13140-13147
Author(s):  
Ian Beaver ◽  
Abdullah Mueen
Keyword(s):  
System Design ◽  
Language Model ◽  
Review System ◽  
Language Understanding ◽  
Model Repair ◽  
Manual Analysis ◽  
User Data ◽  
Human Effort ◽  
Annotation Costs ◽  
And Performance

With the rise of Intelligent Virtual Assistants (IVAs), there is a necessary rise in human effort to identify conversations containing misunderstood user inputs. These conversations uncover error in natural language understanding and help prioritize and expedite improvements to the IVA. As human reviewer time is valuable and manual analysis is time consuming, prioritizing the conversations where misunderstanding has likely occurred reduces costs and speeds improvement. In addition, less conversations reviewed by humans mean less user data is exposed, increasing privacy. We present a scalable system for automated conversation review that can identify potential miscommunications. Our system provides IVA designers with suggested actions to fix errors in IVA understanding, prioritizes areas of language model repair, and automates the review of conversations where desired.Verint - Next IT builds IVAs on behalf of other companies and organizations, and therefore analyzes large volumes of conversational data. Our review system has been in production for over three years and saves our company roughly $1.5 million in annotation costs yearly, as well as shortened the refinement cycle of production IVAs. In this paper, the system design is discussed and performance in identifying errors in IVA understanding is compared to that of human reviewers.

scholarly journals A Model Repair Approach Based on Petri Nets by Constructing Free-Loop Structures

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 24214-24230 ◽  
Author(s):  
Zhaoyang He ◽  
Yuyue Du ◽  
Liang Qi ◽  
Haisen Du
Keyword(s):  
Petri Nets ◽  
Model Repair

scholarly journals A Process Model Repair Approach by Constructing Choice Structures via Logic Petri Nets

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 172387-172402
Author(s):  
Yuhua Xu ◽  
Yuyue Du ◽  
Wenjing Luan ◽  
Liang Qi
Keyword(s):  
Petri Nets ◽  
Process Model ◽  
Model Repair

scholarly journals Model Repair for Markov Decision Processes

2013 ◽  
Author(s):  
Taolue Chen ◽  
Ernst Moritz Hahn ◽  
Tingting Han ◽  
Marta Kwiatkowska ◽  
Hongyang Qu ◽  
...  
Keyword(s):  
Markov Decision Processes ◽  
Decision Processes ◽  
Model Repair ◽  
Markov Decision

scholarly journals Automated model repair for distributed programs

2012 ◽  
Vol 43 (2) ◽  
pp. 85-107 ◽  
Author(s):  
Borzoo Bonakdarpour ◽  
Sandeep S. Kulkarni
Keyword(s):  
Distributed Programs ◽  
Model Repair

scholarly journals An incremental model repair approach to timed discrete event systems

2017 ◽  
Vol 50 (1) ◽  
pp. 13636-13641 ◽  
Author(s):  
Basile F. ◽  
Chiacchio P. ◽  
Coppola J.
Keyword(s):  
Discrete Event Systems ◽  
Discrete Event ◽  
Model Repair ◽  
Incremental Model ◽  
Event Systems

CAD model repair using knowledge-guided NURBS

2012 ◽  
Vol 29 (4) ◽  
pp. 477-486 ◽  
Author(s):  
Khairan Rajab ◽  
Les A. Piegl ◽  
Volha Smarodzinava
Keyword(s):  
Cad Model ◽  
Model Repair
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