EDBT 2026 Demo / reviewers in the wild / expert
Auday Berro
dblp:300/4449
· DBLP profile ↗
4ranked-venue papers in the field
3as first author
4since 2021 · last 2025
0000-0003-2411-5761ORCID · corroborated
Domains — venue-derived; a paper can count in several
Business Process & Enterprise Data · 2 (1 first)Database Systems & Data Management · 1 (1 first)Data Mining & Knowledge Discovery · 1 (1 first)
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | LLMs to Replace Crowdsourcing in Generating Syntactically Diverse Paraphrases for Task-Oriented Chatbots
Auday Berro, Vitor Gaboardi Dos Santos, Boualem Benatallah, Khalid Benabdeslem |
CAiSE (1) | 1 |
| 2024 | Error Types in Transformer-Based Paraphrasing Models: A Taxonomy, Paraphrase Annotation Model and Dataset
Auday Berro, Boualem Benatallah, Yacine Gaci, Khalid Benabdeslem |
ECML/PKDD (1) | 1 |
| 2022 | Crowdsourcing Syntactically Diverse Paraphrases with Diversity-Aware Prompts and Workflows
Jorge Ramírez, Marcos Báez, Auday Berro, Boualem Benatallah, Fabio Casati |
CAiSE | 3 |
| 2021 | An Extensible and Reusable Pipeline for Automated Utterance ParaphrasesabstractIn this demonstration paper we showcase an extensible and reusable pipeline for automatic paraphrase generation , i.e., reformulating sentences using different words. Capturing the nuances of human language is fundamental to the effectiveness of Conversational AI systems, as it allows them to deal with the different ways users can utter their requests in natural language. Traditional approaches to utterance paraphrasing acquisition, such as hiring experts or crowd-sourcing, involve processes that are often costly or time consuming, and with their own trade-offs in terms of quality. Automatic paraphrasing is emerging as an attractive alternative that promises a fast, scalable and cost-effective process. In this paper we showcase how our extensible and reusable pipeline for automated utterance paraphrasing can support the development of Conversational AI systems by integrating and extending existing techniques under an unified and configurable framework. Auday Berro, Mohammad-ali Yaghub Zade Fard, Marcos Báez, Boualem Benatallah, Khalid Benabdeslem |
Proc. VLDB Endow. | 1 |