User profiles for Silvia Terragni
Silvia TerragniUpwork Verified email at upwork.com Cited by 918 |
Cross-lingual contextualized topic models with zero-shot learning
Many data sets (eg, reviews, forums, news, etc.) exist parallelly in multiple languages. They
all cover the same content, but the linguistic differences make it impossible to use traditional, …
all cover the same content, but the linguistic differences make it impossible to use traditional, …
Pre-training is a hot topic: Contextualized document embeddings improve topic coherence
Topic models extract groups of words from documents, whose interpretation as a topic
hopefully allows for a better understanding of the data. However, the resulting word groups are …
hopefully allows for a better understanding of the data. However, the resulting word groups are …
In-context learning user simulators for task-oriented dialog systems
This paper presents a novel application of large language models in user simulation for task-oriented
dialog systems, specifically focusing on an in-context learning approach. By …
dialog systems, specifically focusing on an in-context learning approach. By …
Contrastive language-image pre-training for the italian language
CLIP (Contrastive Language-Image Pre-training) is a very recent multi-modal model that jointly
learns representations of images and texts. The model is trained on a massive amount of …
learns representations of images and texts. The model is trained on a massive amount of …
Which matters most? comparing the impact of concept and document relationships in topic models
Topic models have been widely used to discover hidden topics in a collection of documents.
In this paper, we propose to investigate the role of two different types of relational …
In this paper, we propose to investigate the role of two different types of relational …
Constrained relational topic models
Relational topic models (RTM) have been widely used to discover hidden topics in a collection
of networked documents. In this paper, we introduce the class of Constrained Relational …
of networked documents. In this paper, we introduce the class of Constrained Relational …
OCTIS: Comparing and optimizing topic models is simple!
In this paper, we present OCTIS, a framework for training, analyzing, and comparing Topic
Models, whose optimal hyper-parameters are estimated using a Bayesian Optimization …
Models, whose optimal hyper-parameters are estimated using a Bayesian Optimization …
Contrastive language and vision learning of general fashion concepts
The steady rise of online shopping goes hand in hand with the development of increasingly
complex ML and NLP models. While most use cases are cast as specialized supervised …
complex ML and NLP models. While most use cases are cast as specialized supervised …
BETOLD: A task-oriented dialog dataset for breakdown detection
S Terragni, B Guedes, A Manso… - Proceedings of the …, 2022 - aclanthology.org
Task-Oriented Dialog (TOD) systems often suffer from dialog breakdowns-situations in which
users cannot or do not want to proceed with the conversation. Ideally TOD systems should …
users cannot or do not want to proceed with the conversation. Ideally TOD systems should …
Word embedding-based topic similarity measures
Topic models aim at discovering a set of hidden themes in a text corpus. A user might be
interested in identifying the most similar topics of a given theme of interest. To accomplish this …
interested in identifying the most similar topics of a given theme of interest. To accomplish this …