We investigate grounded language learning through real-world data, by modelling a teacher-learner dynamics through the natural interactions occurring between users and search engines.
Spotting a lie is challenging but has an enormous potential impact on security as well as private and public safety. Several NLP methods have been proposed to classify texts as truthful or deceptive. In most cases, however, the target texts' …
We introduce a novel topic modeling method that can make use of contextulized embeddings (e.g., BERT) to do zero-shot cross-lingual topic modeling.
We introduce a novel topic modeling method that can provide highly coherent topics thanks to the use of contextualized embeddings.
In this paper we work on aligning product embeddings that come from different shops. We use techniques from machine translation to provide an effective method for alignment.
We introduce a novel model for word embedding alignment and test it on temporal word embeddings obtaining SOTA results.
Knowledge Graphs (KG) are widely used abstractions to represent entity-centric knowledge. Approaches to embed entities, entity types and relations represented in the graph into vector spaces - often referred to as KG embeddings - have become …
Knowledge Graphs (KG) represent a large amount of Semantic Associations (SAs), i.e., chains of relations that may reveal interesting and unknown connections between different types of entities. Applications for the contextual exploration of KGs help …