Silvia Terragni

Orcid: 0000-0002-0358-1854

According to our database1, Silvia Terragni authored at least 15 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
Reliable LLM-based User Simulator for Task-Oriented Dialogue Systems.
CoRR, 2024

2023
The role of hyper-parameters in relational topic models: Prediction capabilities vs topic quality.
Inf. Sci., 2023

In-Context Learning User Simulators for Task-Oriented Dialog Systems.
CoRR, 2023

Contrastive Language-Image Pre-training for the Italian Language.
Proceedings of the 9th Italian Conference on Computational Linguistics, Venice, Italy, November 30, 2023

2022
FashionCLIP: Connecting Language and Images for Product Representations.
CoRR, 2022

One Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian Optimization.
CoRR, 2022

2021
Contrastive Language-Image Pre-training for the Italian Language.
CoRR, 2021

An Empirical Analysis of Topic Models: Uncovering the Relationships between Hyperparameters, Document Length and Performance Measures.
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), 2021

Word Embedding-Based Topic Similarity Measures.
Proceedings of the Natural Language Processing and Information Systems, 2021

OCTIS: Comparing and Optimizing Topic models is Simple!
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, 2021

Cross-lingual Contextualized Topic Models with Zero-shot Learning.
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021

OCTIS 2.0: Optimizing and Comparing Topic Models in Italian Is Even Simpler!
Proceedings of the Eighth Italian Conference on Computational Linguistics, 2021

Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence.
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

2020
Constrained Relational Topic Models.
Inf. Sci., 2020

Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models.
Proceedings of the First Workshop on Insights from Negative Results in NLP, 2020


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