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2020 – today
- 2024
[i7]Ivan Sekulic, Silvia Terragni, Victor Guimarães, Nghia Khau, Bruna Guedes, Modestas Filipavicius, André Ferreira Manso, Roland Mathis:
Reliable LLM-based User Simulator for Task-Oriented Dialogue Systems. CoRR abs/2402.13374 (2024)- 2023
- [j2]Silvia Terragni, Antonio Candelieri, Elisabetta Fersini:
The role of hyper-parameters in relational topic models: Prediction capabilities vs topic quality. Inf. Sci. 632: 252-268 (2023) - [c8]Federico Bianchi, Giuseppe Attanasio, Raphael Pisoni, Silvia Terragni, Gabriele Sarti, Dario Balestri:
Contrastive Language-Image Pre-training for the Italian Language. CLiC-it 2023 - [i6]Silvia Terragni, Modestas Filipavicius, Nghia Khau, Bruna Guedes, André Ferreira Manso, Roland Mathis:
In-Context Learning User Simulators for Task-Oriented Dialog Systems. CoRR abs/2306.00774 (2023) - 2022
- [i5]Silvia Terragni, Ismail Harrando, Pasquale Lisena, Raphaël Troncy, Elisabetta Fersini:
One Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian Optimization. CoRR abs/2202.07631 (2022) - [i4]Patrick John Chia, Giuseppe Attanasio, Federico Bianchi, Silvia Terragni, Ana Rita Magalhães, Diogo Gonçalves, Ciro Greco, Jacopo Tagliabue:
FashionCLIP: Connecting Language and Images for Product Representations. CoRR abs/2204.03972 (2022) - 2021
- [c7]Federico Bianchi, Silvia Terragni, Dirk Hovy:
Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence. ACL/IJCNLP (2) 2021: 759-766 - [c6]Silvia Terragni, Elisabetta Fersini:
OCTIS 2.0: Optimizing and Comparing Topic Models in Italian Is Even Simpler! CLiC-it 2021 - [c5]Silvia Terragni, Elisabetta Fersini, Bruno Giovanni Galuzzi, Pietro Tropeano, Antonio Candelieri:
OCTIS: Comparing and Optimizing Topic models is Simple! EACL (System Demonstrations) 2021: 263-270 - [c4]Federico Bianchi, Silvia Terragni, Dirk Hovy, Debora Nozza
, Elisabetta Fersini:
Cross-lingual Contextualized Topic Models with Zero-shot Learning. EACL 2021: 1676-1683 - [c3]Silvia Terragni, Elisabetta Fersini, Enza Messina:
Word Embedding-Based Topic Similarity Measures. NLDB 2021: 33-45 - [c2]Silvia Terragni, Elisabetta Fersini:
An Empirical Analysis of Topic Models: Uncovering the Relationships between Hyperparameters, Document Length and Performance Measures. RANLP 2021: 1408-1416 - [i3]Federico Bianchi, Giuseppe Attanasio, Raphael Pisoni, Silvia Terragni, Gabriele Sarti, Sri Lakshmi:
Contrastive Language-Image Pre-training for the Italian Language. CoRR abs/2108.08688 (2021) - 2020
- [j1]Silvia Terragni, Elisabetta Fersini, Enza Messina:
Constrained Relational Topic Models. Inf. Sci. 512: 581-594 (2020) - [c1]Silvia Terragni, Debora Nozza, Elisabetta Fersini, Enza Messina:
Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models. Insights 2020: 32-40 - [i2]Federico Bianchi, Silvia Terragni, Dirk Hovy:
Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence. CoRR abs/2004.03974 (2020) - [i1]Federico Bianchi, Silvia Terragni, Dirk Hovy, Debora Nozza, Elisabetta Fersini:
Cross-lingual Contextualized Topic Models with Zero-shot Learning. CoRR abs/2004.07737 (2020)
Coauthor Index
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