298 citations · 471 across the 27 of their papers we have counts for
8 papers · 1 filter
BERT Goes Shopping: Comparing Distributional Models for Product Representations
Federico Bianchi, Bingqing Yu, Jacopo Tagliabue
Word embeddings (e.g., word2vec) have been applied successfully to eCommerce products through~\textit{prod2vec}. Inspired by the recent performance improvements on several NLP task…
Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario
Federico Bianchi, Jacopo Tagliabue, Bingqing Yu +2
This paper addresses the challenge of leveraging multiple embedding spaces for multi-shop personalization, proving that zero-shot inference is possible by transferring shopping int…
Knowledge Graph Embeddings and Explainable AI
Federico Bianchi, Gaetano Rossiello, Luca Costabello +2
Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces. In this chapter, we intr…
Compass-aligned Distributional Embeddings for Studying Semantic Differences across Corpora
Federico Bianchi, Valerio Di Carlo, Paolo Nicoli +1
Word2vec is one of the most used algorithms to generate word embeddings because of a good mix of efficiency, quality of the generated representations and cognitive grounding. Howev…
Cross-lingual Contextualized Topic Models with Zero-shot Learning
Federico Bianchi, Silvia Terragni, Dirk Hovy +2
Many data sets (e.g., reviews, forums, news, etc.) exist parallelly in multiple languages. They all cover the same content, but the linguistic differences make it impossible to use…
Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence
Federico Bianchi, Silvia Terragni, Dirk Hovy
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…