The goal of text-to-text generation is to make machines express like a human in many
applications such as conversation, summarization, and translation. It is one of the most …

The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately,
many application domains do not have access to big data because acquiring data involves a …

Artificial intelligence (AI) is increasingly being adopted by organizations, yet implementation
is often carried out without careful consideration of the employees who will be working along …

From AlphaGo to ChatGPT, the field of AI has launched a series of remarkable
achievements in recent years. Analyzing, comparing, and summarizing these achievements …

The majority of conversations a dialogue agent sees over its lifetime occur after it has
already been trained and deployed, leaving a vast store of potential training signal …

Traditional machine learning, mainly supervised learning, follows the assumptions of closed-
world learning, ie, for each testing class, a training class is available. However, such …

Managing and evolving a chatbot's content is a laborious process and there is still a lack of
standardization. In this context of standardization, the absence of a management process …

Due to advancements in data collection, storage, and processing techniques, machine
learning has become a thriving and dominant paradigm. However, one of its main …

Most of knowledge graph completion (KGC) models are designed for static KGs where entity
and relation sets are fixed. These approaches are inherently transductive because they …

Dialogue systems are increasingly using knowledge bases (KBs) storing real-world facts to
help generate quality responses. However, as the KBs are inherently incomplete and remain …