The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

While BERT is an effective method for learning monolingual sentence embeddings for
semantic similarity and embedding based transfer learning (Reimers and Gurevych, 2019) …

We introduce two pre-trained retrieval focused multilingual sentence encoding models,
respectively based on the Transformer and CNN model architectures. The models embed …

Building conversational systems in new domains and with added functionality requires
resource-efficient models that work under low-data regimes (ie, in few-shot setups) …

General-purpose pretrained sentence encoders such as BERT are not ideal for real-world
conversational AI applications; they are computationally heavy, slow, and expensive to train …

This is the first survey of the active area of AI research that focuses on privacy issues in
Large Language Models (LLMs). Specifically, we focus on work that red-teams models to …

In the last decade, the field of argument mining has grown notably. However, only relatively
few studies have investigated argumentation in social media and specifically on Twitter …

Learning query and item representations is important for building large scale
recommendation systems. In many real applications where there is a huge catalog of items …

In the era of pre-trained language models, Transformers are the de facto choice of model
architectures. While recent research has shown promise in entirely convolutional, or CNN …

In this paper, we present an approach to learn multilingual sentence embeddings using a bi-
directional dual-encoder with additive margin softmax. The embeddings are able to achieve …