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.
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.
Sentiment analysis is a common task to understand people's reactions online. Still, we often need more nuanced information: is the post negative because the user is angry or because they are sad? An abundance of approaches has been introduced for …
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.