Papers by Cezar Pendus
The TechQA Dataset (2020.acl-main)
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Vittorio Castelli, Rishav Chakravarti, Saswati Dana, Anthony Ferritto, Radu Florian, Martin Franz, Dinesh Garg, Dinesh Khandelwal, Scott McCarley, Michael McCawley, Mohamed Nasr, Lin Pan, Cezar Pendus, John Pitrelli, Saurabh Pujar, Salim Roukos, Andrzej Sakrajda, Avi Sil, Rosario Uceda-Sosa, Todd Ward, Rong Zhang
| Challenge: | TECHQA is a domain-adaptation question answering dataset for the technical support domain. |
| Approach: | They propose a domain-adaptation question-answering dataset for the technical support domain that contains actual questions posed by users on a technical forum . |
| Outcome: | The TECHQA dataset highlights two real-world issues from the automated customer support domain. |
CFO: A Framework for Building Production NLP Systems (D19-3)
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Rishav Chakravarti, Cezar Pendus, Andrzej Sakrajda, Anthony Ferritto, Lin Pan, Michael Glass, Vittorio Castelli, J. William Murdock, Radu Florian, Salim Roukos, Avi Sil
| Challenge: | Using a new orchestration framework, we build, test, and deploy interactive NLP and IR systems to production environments. |
| Approach: | They introduce a new orchestration framework for building, experimenting with, and deploying interactive NLP and IR systems to production environments. |
| Outcome: | The proposed framework is well suited to a variety of use cases but is not suitable for academic benchmarking or industry specific use cases. |
SYGMA: A System for Generalizable and Modular Question Answering Over Knowledge Bases (2022.findings-emnlp)
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Sumit Neelam, Udit Sharma, Hima Karanam, Shajith Ikbal, Pavan Kapanipathi, Ibrahim Abdelaziz, Nandana Mihindukulasooriya, Young-Suk Lee, Santosh Srivastava, Cezar Pendus, Saswati Dana, Dinesh Garg, Achille Fokoue, G P Shrivatsa Bhargav, Dinesh Khandelwal, Srinivas Ravishankar, Sairam Gurajada, Maria Chang, Rosario Uceda-Sosa, Salim Roukos, Alexander Gray, Guilherme Lima, Ryan Riegel, Francois Luus, L V Subramaniam
| Challenge: | Knowledge Base Question Answering (KBQA) systems have limited generalizability across knowledge bases and multiple reasoning types. |
| Approach: | They propose a modular approach for KBQA that is built on a framework adaptable to multiple knowledge bases and reasoning types. |
| Outcome: | The proposed approach is generalized across multiple knowledge bases and reasoning types. |