Project Debater APIs: Decomposing the AI Grand Challenge

Roy Bar-Haim, Yoav Kantor, Elad Venezian, Yoav Katz, Noam Slonim

Abstract
Project Debater was revealed in 2019 as the first AI system that can debate human experts on complex topics. Engaging in a live debate requires a diverse set of skills, and Project Debater has been developed accordingly as a collection of components, each designed to perform a specific subtask. Project Debater APIs provide access to many of these capabilities, as well as to more recently developed ones. This diverse set of web services, publicly available for academic use, includes core NLP services, argument mining and analysis capabilities, and higher-level services for content summarization. We describe these APIs and their performance, and demonstrate how they can be used for building practical solutions. In particular, we will focus on Key Point Analysis, a novel technology that identifies the main points and their prevalence in a collection of texts such as survey responses and user reviews.
Anthology ID:
2021.emnlp-demo.31
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Heike Adel, Shuming Shi
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
267–274
Language:
URL:
https://aclanthology.org/2021.emnlp-demo.31
DOI:
10.18653/v1/2021.emnlp-demo.31
Bibkey:
Cite (ACL):
Roy Bar-Haim, Yoav Kantor, Elad Venezian, Yoav Katz, and Noam Slonim. 2021. Project Debater APIs: Decomposing the AI Grand Challenge. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 267–274, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Project Debater APIs: Decomposing the AI Grand Challenge (Bar-Haim et al., EMNLP 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-2024-clasp/2021.emnlp-demo.31.pdf
Video:
 https://preview.aclanthology.org/ingest-2024-clasp/2021.emnlp-demo.31.mp4
Data
IBM-Rank-30k
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